Barrier flow diagnostics through differential mapping

ABSTRACT

A method of abandoning a wellbore can include obtaining a first sample data set within a wellbore, wherein the first sample data set is a sample of an acoustic signal originating within the wellbore; determining a plurality of frequency domain features of the first sample data set; identifying a fluid flow location within the wellbore using the first plurality of frequency domain features; setting a barrier at or above the fluid flow location; obtaining a second sample data set above the barrier, wherein the second sample data set is a sample of an acoustic signal originating within the wellbore above the barrier; determining a second plurality of frequency domain features of the second sample data set; and identifying that a fluid flow rate or flow mechanism at the fluid flow location has been reduced or eliminated and/or identifying another fluid flow location using the second plurality of frequency domain features.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a 35 U.S.C. § 371 national stage application ofPCT/IB2019/055355 filed Jun. 25, 2019, and entitled “Barrier FlowDiagnostics Through Differential Mapping,” which is hereby incorporatedherein by reference in its entirety for all purposes.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

Not applicable.

BACKGROUND

Within a hydrocarbon production well, various fluids such ashydrocarbons, water, gas, and the like can be produced from theformation into the wellbore. The production of the fluid can result inthe movement of the fluids in various downhole regions, including withthe subterranean formation, from the formation into the wellbore, andwithin the wellbore itself. Following production, plugs are positionedin a well to be abandoned in order to prevent leaks of fluid from thewell.

BRIEF SUMMARY OF THE DISCLOSURE

In an embodiment, a method of abandoning a wellbore comprises obtaininga first sample data set within a wellbore, wherein the first sample dataset is a sample of an acoustic signal originating within the wellbore;determining a first plurality of frequency domain features of the firstsample data set; identifying a first fluid flow location within thewellbore using the first plurality of frequency domain features; settinga first barrier at or above the first fluid flow location; obtaining asecond sample data set within the wellbore above the first barrier,wherein the second sample data set is a sample of an acoustic signaloriginating within the wellbore above the first barrier; determining asecond plurality of frequency domain features of the second sample dataset; and identifying that that a fluid flow rate or fluid flow mechanismat the first fluid flow location has been reduced or eliminated and/oridentifying a second fluid flow location within the wellbore using thesecond plurality of frequency domain features. The first sample data andthe second sample data set can comprise a sample of an acoustic signaloriginating within the wellbore, and can be representative of theacoustic signal across a frequency spectrum.

In an embodiment, a system for abandoning a wellbore, the systemcomprising: a receiver unit comprising a processor and a memory, whereinthe receiver unit is configured to receive an acoustic signal from asensor disposed in a wellbore, wherein a processing application isstored in the memory, and wherein the processing application, whenexecuted on the processor, configures the processor to: receive a firstbaseline acoustic signal data set from the sensor, wherein the firstbaseline acoustic signal data set comprises an indication of theacoustic signal received over a first depth interval while the wellboreis shut in; receive a first flowing acoustic signal data set, whereinthe first flowing acoustic signal data set comprises an indication ofthe acoustic signal received over the first depth interval while a firstpressure differential is induced within the wellbore; determine abaseline fluid flow log using the first baseline acoustic signal dataset; determine a flowing fluid flow log using the first flowing acousticsignal data set; subtract the baseline fluid flow log from the flowingfluid flow log to provide a first sample data set; determine a firstplurality of frequency domain features of the first sample data set;identify a first fluid flow location within the wellbore using the firstplurality of frequency domain features; determine a change in a flowrate or flow mechanism at the first fluid flow location using the firstsample data set; and generate an output indicative of the first fluidflow location and a change in the flow rate or flow mechanism at thefirst fluid flow location. The acoustic signal can be indicative of theacoustic signal across a frequency spectrum.

In an embodiment, a method of comparing acoustic signals obtainedbetween different acoustic sensor operations in a wellbore comprises:obtaining a first baseline sample data set over a first depth intervalwithin a wellbore, wherein the first baseline data set is a sample of anacoustic signal originating within the wellbore; determining at leastone frequency domain feature of the first baseline sample data set;inducing a first pressure differential within the wellbore; obtaining afirst acoustic data set over the first depth interval within thewellbore while inducing the first pressure differential; determining atleast one frequency domain feature of the first acoustic data set;subtracting the at least one frequency domain feature of the firstbaseline sample data set from the at least one frequency domain featureof the first acoustic data set to obtain a first sample data set overthe first depth interval; obtaining a second baseline sample data setover a second depth interval within the wellbore, wherein the secondbaseline sample data set is a sample of an acoustic signal originatingwithin the wellbore, wherein the second depth interval overlaps with thefirst depth interval; determining at least one frequency domain featureof the second baseline sample data set; inducing a second pressuredifferential within the wellbore; obtaining a second acoustic data setover the second depth interval within the wellbore while inducing thesecond pressure differential; determining at least one frequency domainfeature of the second acoustic data set; subtracting the at least onefrequency domain feature of the second baseline sample data set from theat least one frequency domain feature of the second acoustic data set toobtain a second sample data set over the second depth interval; andcomparing the second sample data set to the first sample data set overthe second depth interval.

In an embodiment, a system for of comparing acoustic signals obtainedbetween different acoustic sensor operations in a wellbore, the systemcomprising: a receiver unit comprising a processor and a memory, whereinthe receiver unit is configured to receive an acoustic signal from asensor disposed in a wellbore, wherein a processing application isstored in the memory, and wherein the processing application, whenexecuted on the processor, configures the processor to: receive a firstbaseline sample data set over a first depth interval within thewellbore, wherein the first baseline data set is a sample of an acousticsignal originating within the wellbore; determine at least one frequencydomain feature of the first baseline sample data set; receive a firstacoustic data set over the first depth interval within the wellbore,wherein the first acoustic data sat is an acoustic signal obtained whilea first pressure differential is induced within the wellbore; determineat least one frequency domain feature of the first acoustic data set;subtract the at least one frequency domain feature of the first baselinesample data set from the at least one frequency domain feature of thefirst acoustic data set to obtain a first sample data set over the firstdepth interval; receive a second baseline sample data set over a seconddepth interval within the wellbore, wherein the second baseline sampledata set is a sample of an acoustic signal originating within thewellbore, wherein the second depth interval overlaps with the firstdepth interval; determine at least one frequency domain feature of thesecond baseline sample data set; receive a second acoustic data set overthe second depth interval within the wellbore, wherein the secondacoustic data sat is an acoustic signal obtained while a second pressuredifferential is induced within the wellbore; determine at least onefrequency domain feature of the second acoustic data set; subtract theat least one frequency domain feature of the second baseline sample dataset from the at least one frequency domain feature of the secondacoustic data set to obtain a second sample data set over the seconddepth interval; compare the second sample data set to the first sampledata set over the second depth interval; and generate an outputindicative of the comparison between the second sample data set and thefirst sample data set.

In an embodiment, a method of abandoning a wellbore comprises: obtaininga first sample data set over a first depth interval within a wellbore,wherein the first sample data set comprises a first acoustic data sethaving a first baseline acoustic sample data set subtracted therefrom,wherein the first acoustic data set is obtained over the first depthinterval while a first pressure differential is induced in the wellbore,and wherein the first baseline acoustic sample data set is obtained overthe first depth interval while the wellbore is shut in; identifying afluid flow location within the first depth interval using the firstsample data set; obtaining a second sample data set over a second depthinterval within a wellbore, wherein the second sample data set isobtained after a barrier is set at or above the fluid flow location,wherein the second sample data set comprises a second acoustic data sethaving a second baseline acoustic sample data set subtracted therefrom,wherein the second acoustic data set is obtained over the second depthinterval while a second pressure differential is induced in thewellbore, wherein the second baseline acoustic sample data set isobtained over the second depth interval while the wellbore is shut in,and wherein the second depth interval is overlaps the first depthinterval; comparing the first sample data set to the second sample dataset; and determining whether or not fluid flow at the fluid flowlocation is substantially blocked by the barrier.

In an embodiment, a system for abandoning a wellbore, the systemcomprising: a receiver unit comprising a processor and a memory, whereinthe receiver unit is configured to receive an acoustic signal from asensor disposed in a wellbore, wherein a processing application isstored in the memory, and wherein the processing application, whenexecuted on the processor, configures the processor to: receive a firstbaseline acoustic sample data set and a first acoustic data set from thesensor, wherein the first acoustic data set is an acoustic signalobtained over a first depth interval while a first pressure differentialis induced in the wellbore, and wherein the first baseline acousticsample data set is an acoustic signal obtained over the first depthinterval while the wellbore is shut in, determine a first sample dataset over a first depth interval within the wellbore, wherein the firstsample data set comprises the first acoustic data set having the firstbaseline acoustic sample data set subtracted therefrom; identify a fluidflow location within the first depth interval using the first sampledata set; receive a second baseline acoustic sample data set and asecond acoustic data set from the sensor, wherein the second acousticdata set is an acoustic signal obtained over a second depth intervalwhile a second pressure differential is induced in the wellbore andafter a barrier is set at or above the fluid flow location, and whereinthe second baseline acoustic sample data set is an acoustic signalobtained over the second depth interval while the wellbore is shut inand after the barrier is set at or above the fluid flow location;determine a second sample data set over the second depth interval withinthe wellbore, wherein the second sample data set comprises the secondacoustic data set having the second baseline acoustic sample data setsubtracted therefrom; compare the first sample data set to the secondsample data set; and determine whether or not fluid flow at the fluidflow location is substantially blocked by the barrier, and generate anoutput indicative the determination of whether or not the fluid flow atthe fluid flow location is substantially blocked by the barrier.

These and other features will be more clearly understood from thefollowing detailed description taken in conjunction with theaccompanying drawings and claims.

Embodiments described herein comprise a combination of features andadvantages intended to address various shortcomings associated withcertain prior devices, systems, and methods. The foregoing has outlinedrather broadly the features and technical advantages of the invention inorder that the detailed description of the invention that follows may bebetter understood. The various characteristics described above, as wellas other features, will be readily apparent to those skilled in the artupon reading the following detailed description, and by referring to theaccompanying drawings. It should be appreciated by those skilled in theart that the conception and the specific embodiments disclosed may bereadily utilized as a basis for modifying or designing other structuresfor carrying out the same purposes of the invention. It should also berealized by those skilled in the art that such equivalent constructionsdo not depart from the spirit and scope of the invention as set forth inthe appended claims.

BRIEF DESCRIPTION OF THE DRAWINGS

For a detailed description of the preferred embodiments of theinvention, reference will now be made to the accompanying drawings inwhich:

FIG. 1 is a schematic, cross-sectional illustration of a downholewellbore environment according to embodiments of this disclosure.

FIG. 2 is a schematic, cross-sectional illustration of another downholewellbore environment according to embodiments of this disclosure.

FIG. 3A is a schematic view of a wellbore environment 100B prior toplacement of well barriers.

FIG. 3B is a schematic view of a wellbore environment 100C afterplacement of well barriers.

FIG. 4A is a schematic, cross-sectional view of an embodiment of a wellwith a wellbore tubular having an optical fibre associated therewith.

FIG. 48 is a schematic, cross-sectional view of another embodiment of awell with a wellbore tubular having an optical fibre associatedtherewith.

FIG. 5 illustrates an embodiment of a schematic processing flow for anacoustic signal.

FIGS. 6A and 6B illustrate exemplary acoustic depth-time block graphs.

FIGS. 7A, 7B, and 7C illustrate exemplary filtered acoustic depth-timegraphs.

FIG. 8 illustrates an exemplary fluid flow log according to embodimentsof this disclosure.

FIG. 9 schematically illustrates a computer that can be used to carryout various steps according to some embodiments of this disclosure.

FIG. 10 is a schematic showing baseline logs for three runs of Example1: Run 1 prior to placement of a first well barrier element WBE1,referred to in FIG. 10 as “Pre WBE1 placement; Run 2 after placement offirst well barrier element WBE1, referred to in FIG. 10 as “Post WBE1placement”; and Run 3 after placement of second and third well barrierelements WBE2/3, referred to in FIG. 10 as “Post WBE2/3 placement.”

FIG. 11 is a schematic showing the DAS logs (e.g., the acoustic logs)for the baseline and C bleed of Run 3 (e.g., after setting of second andthird well barrier elements WBE2/3) of Example 1.

FIG. 12 is a schematic of the DAS logs obtained during the B bleeds ofRun 2 (e.g., after placement of first well barrier element WBE1) and Run3 (e.g., after placement of second and third well barrier elementsWBE2/3) of Example 1.

FIG. 13 is a schematic showing the DAS logs for the baseline, the Bbleed and the C bleed for Run 3 (e.g., after placement of second andthird well barrier elements WBE2/3) of Example 1.

FIG. 14A is a schematic of the DAS logs for Run 1 (e.g., prior toplacement of first well barrier element WBE1), including one houraveraged comparisons for the baseline, the B bleed, and the C bleed ofExample 1.

FIG. 14B is a schematic of the DAS logs for the baseline corrected Cbleed (e.g., the C bleed minus the baseline) of Run 1 (e.g., prior toplacement of first well barrier element WBE1) and a baseline smoothedlog of the C bleed of Run 1 of Example 1.

FIG. 15A is a schematic of the DAS logs for Run 3 (e.g., after placementof the second and third well barrier elements WBE2/3), including onehour averaged comparisons for the baseline, the B bleed, and the C bleedof Example 1.

FIG. 15B is a schematic of the DAS logs for the baseline corrected Cbleed (e.g., the C bleed minus the baseline) of Run 3 (i.e., afterplacement of second and third well barrier elements WBE2/3) and abaseline smoothed log of the C bleed of Run 3 of Example 1.

FIG. 16 is a schematic of the DAS logs of the baseline smoothed C bleedsof Run 1 (e.g., prior to placement of first well barrier element WBE1)and Run 3 (i.e., after placement of second and third well barrierelements WBE2/3) of Example 1.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

Unless otherwise specified, any use of any form of the terms “connect,”“engage,” “couple,” “attach,” or any other term describing aninteraction between elements is not meant to limit the interaction todirect interaction between the elements and may also include indirectinteraction between the elements described. In the following discussionand in the claims, the terms “including” and “comprising” are used in anopen-ended fashion, and thus should be interpreted to mean “including,but not limited to . . . ”. Reference to up or down will be made forpurposes of description with “up,” “upper,” “upward,” “upstream,” or“above” meaning toward the surface of the wellbore and with “down,”“lower,” “downward,” “downstream,” or “below” meaning toward theterminal end of the well, regardless of the wellbore orientation.Reference to inner or outer will be made for purposes of descriptionwith “in,” “inner,” or “inward” meaning towards the central longitudinalaxis of the wellbore and/or wellbore tubular, and “out,” “outer,” or“outward” meaning towards the wellbore wall. As used herein, the term“longitudinal” or “longitudinally” refers to an axis substantiallyaligned with the central axis of the wellbore tubular, and “radial” or“radially” refer to a direction perpendicular to the longitudinal axis.The various characteristics mentioned above, as well as other featuresand characteristics described in more detail below, will be readilyapparent to those skilled in the art with the aid of this disclosureupon reading the following detailed description of the embodiments, andby referring to the accompanying drawings.

Disclosed herein is a real time signal processing architecture thatallows for the identification of the presence, location, rate, and flowmechanism of various downhole fluid flows (fluid flow refers to fluidinflow, fluid flow within the wellbore, within an annulus, or anycombination thereof, which may be indicative of a “leak”), whereby oneor more well barriers can be positioned at or above the one or morefluid flow locations during plugging and abandonment (P&A) operations.The signal processing architecture can be utilized to identify one ormore fluid flow events including fluid flow detection, pressure sourceidentification, flow path identification, and phase detection of an flowfluid in the wellbore (within a casing, within an annulus, etc.), theformation (e.g., overburden monitoring, etc.), or moving between theformation and wellbore. As used herein, the term “fluid flow mechanism”can refer to the fluid flow pathway, source, and/or flow type or phaseof a flowing fluid. As used herein, the term “real time” refers to atime that takes into account various communication and latency delayswithin a system, and can include actions taken within about ten seconds,within about thirty seconds, within about a minute, within about fiveminutes, or within about ten minutes of the action occurring.

In general, zonal isolation and well integrity management are concernsnot only from the standpoint of operational risk and requirements, butalso from an environmental impact perspective. Fluid flow detectiontechniques can include the use of temperature sensors, pressure sensors,casing collar locators, multi-finger calipers, spinners, and sometimes,density measurement tools deployed in well using interventiontechnologies, as well as other non-invasive evaluation/assessmenttechniques for detecting flow behind casing (e.g., temperature logging,ultrasonic imaging, oxygen activation (for detection of water flowbehind casing) with neutrons, and the like).

While one or a combination of these tools may help provide aqualitative, and sometimes quantitative, estimates of fluid flowlocations between the production tubing and the production casing, thesemethods suffer from being ‘point’ measurement tools (i.e., tools thatcan only transduce a single physical parameter at a certain discretelocation/depth at any one instance in time). This means that the fluidflows/leaks may not be captured accurately or captured at all unless thetools are positioned at the right location at the right time and/orunless the fluid flow or leak is large enough to generate a transduciblesignal. This typically results in longer data acquisition times andlimited representations, which can often impede decision making andsupport. None of these tools offer the capability to monitor the flow ofhydrocarbons behind multiple barriers, for example, in the casing-casingannuli, and this presents a challenge in maintaining well integrity.Multi-finger calipers are also often used to investigate any diametervariations along the tubing but this process does not quantify theextent, rate, or phase of leaking fluid. This also only provides anindication of potential fluid flow location based on mechanicalassessment of the tubing. Each of these methods generally only providean indication of a fluid flow location and do not provide the means toassess changes in fluid flow rate or fluid flow mechanism (e.g., changesin fluid flow pathways, sources, flow types, etc.).

As described in more detail herein, distributed fibre optic (DFO)sensors for well integrity assessment use the fibre to monitorproperties along the length of a wellbore. Similarly, distributedtemperature sensing systems (DTS) can be used to measure the temperaturealong the wellbore. The main advantage of these DFO sensors is that themeasurement can be made along the entire length of the wellbore overlong periods of time as the entire deployed fibre cable is the sensor.This can avoid the need to move the tool and aid in more economicaloperations. The full wellbore coverage would also enable studies offluid flow evolution through time and depth, consequently enablingprecise identification of when and where fluid flows occur, rather thanpiecing together the picture from various steps in the loggingoperation. The use of DTS for leak detection however, brings a fewlimitations including: 1) the use of thermal profiles alone for leakidentification often results in inconclusive results, and 2) it isdifficult to achieve controlled shut in versus flowing conditionsoutside of casing to compare and determine leak locations from baselinethermal profiles.

As disclosed herein, a new approach to plugging and abandoning a well isdescribed using Distributed Acoustic Sensors (DAS) as the primary datainput. This type of system offers not only identification of leaks andfluid flow behind casing, but also enables categorization of theseevents in real time or near real time. A data processing architecture isalso described that processes voluminous DAS data in near real time(e.g., within a second, within ten seconds, etc.) to identify andclassify leaks and other “fluid flow events” indicative of well barrierperformance with a single fibre optic cable deployed in well. Inembodiments, the data can also be used in conjunction with surface andperipheral sensor data to enable semi-quantitative assessments of fluidflow rates.

As further disclosed herein, the DAS data can be used with additionalsensor data such as pressure data as the primary sensor inputs fordetermining in-well and near wellbore fluid flows. The processingmethodology uses a fluid flow event detection algorithm that detects andcaptures acoustic events that are then processed in real-time using aspectral descriptor framework for signature recognition andidentification of fluid flow. In embodiments, the outputs of the fluidflow event detection algorithm can then be correlated in time with theadditional sensor data (e.g., the pressure gauge measurements). Thecorrelation of the signals can enable identification of: a pressuresource, a location of a leak, a flow rate of the leak, a leak flow path,and/or a predominant phase of a flowing fluid, and thus be utilized todetermine where to set a barrier for well abandonment and/or determinewhether or not well barrier placement has successfully plugged the well(e.g., that placement of one or more well barriers has reduced oreliminated fluid flow at one or more identified fluid flow locations).

The method may also allow for monitoring fluid flows behind multiplebarriers which are usually not detected using conventional leakdetection diagnostics tools. This ability enables monitoring ofhydrocarbon migration up pathways adjacent to wellbores to shallowerzones (cross-flow) and/or into well annuli, thereby enabling real timemonitoring of fluid movements in the formation and/or annuli andevaluating how to best plug such fluid flows for well abandonment.

As described in more detail herein, the system comprises a DASinterrogator connected to the fibre optic cable deployed in the well.Various sensors (e.g., the distributed fibre optic acoustic sensors,etc.) can be used to obtain an acoustic sampling at various points alongthe wellbore. The acoustic sample can then be processed using signalprocessing architecture with various feature extraction techniques(e.g., spectral feature extraction techniques) to obtain a measure ofone or more frequency domain features that enable selectively extractingthe acoustic signals of interest from background noise and consequentlyaiding in improving the accuracy of the identification of the movementof fluids and/or solids (e.g., liquid ingress locations, gas influxlocations, constricted fluid flow locations, etc.) in real time. As usedherein, various frequency domain features can be obtained from theacoustic signal. In some contexts the frequency domain features can alsobe referred to as spectral features or spectral descriptors. The signalprocessing techniques described herein can also help to address thebig-data problem through intelligent extraction of data (rather thancrude decimation techniques) to considerably reduce real time datavolumes at the collection and processing site (e.g., by over 100 times,over 500 times, or over 1000 times, or over 10,000 times reduction).

The acoustic signal can be obtained in a manner that allows for a signalto be obtained along the entire wellbore or a portion of interest. Fibreoptic distributed acoustic sensors (DAS) capture acoustic signalsresulting from downhole events such as gas influx, liquid influx, fluidflow past restrictions, and the like as well as other backgroundacoustics as well. This mandates the need for a robust signal processingprocedure that distinguishes acoustic signals resulting from events ofinterest from other noise sources to avoid false positives in theresults. This in turn results in a need for a clearer understanding ofthe acoustic fingerprint of in-well event of interest (e.g., fluid flowdetection, leak detection, etc.) in order to be able to segregate anoise resulting from an event of interest from other ambient acousticbackground noise. As used herein, the resulting acoustic fingerprint ofa particular event can also be referred to as a spectral signature. Thespectral signature can be defined by a plurality of different frequencydomain features and/or combination and modifications thereof, andcorresponding thresholds or ranges for the plurality of differentfrequency domain features and/or combination and modifications thereof,as described in more detail herein.

The ability to identify various fluid flow events in the wellbore mayallow for appropriate actions to be taken in order to plug the leaks andprepare the well for abandonment. For example, a barrier can bepositioned at or above one or more identified fluid flow locations, andthe DAS system utilized to determine whether or not the barrier issuccessful at reducing or eliminating the fluid flow at the one or morefluid flow locations. As described herein, frequency domain features(e.g., also referred to as spectral descriptors) can be used with DASacoustic data processing in real time to provide various downholesurveillance applications. More specifically, the data processingtechniques can be applied for various downhole fluid profiling such asevents including fluid flow/inflow/outflow detection, fluid phasesegregation, well integrity monitoring, in-well leak detection (e.g.,downhole casing and tubing leak detection, leaking fluid phaseidentification, etc.), annular fluid flow detection, overburdenmonitoring, fluid flow detection behind a casing, fluid inducedhydraulic fracture detection in the overburden, and the like, and canthus be utilized to determine a degree of success in blocking fluidflow(s) at one or more identified fluid flow locations via the settingof one or more well barriers. Such events may be referred to herein as“fluid flow” events.

In addition to the use of DAS data, additional sensor data such aspressure sensors and/or flow sensors can be used to obtain data withinthe wellbore. As an example, a flow sensor or pressure sensor can beused to detect fluid flow within the wellbore and/or an annulus withinthe wellbore. The sensors can be used with controlled shut-in and/orflow conditions to correlate in time the resulting pressure and/or flowconditions with the processed DAS data. The resulting correlation canthen be used to determine a presence (or absence) and/or location offluid flow.

Referring now to FIG. 1, an example of a wellbore operating environment100 is shown. As will be described in more detail below, embodiments ofcompletion assemblies comprising distributed acoustic sensor (DAS)system in accordance with the principles described herein can bepositioned in environment 100.

As shown in FIG. 1, exemplary environment 100 includes a wellbore 114traversing a subterranean formation 102, casing 112 lining at least aportion of wellbore 114, and a tubular 120 extending through wellbore114 and casing 112. A plurality of spaced screen elements or assemblies118 are provided along tubular 120. In addition, a plurality of spacedzonal isolation devices 117 and gravel packs 122 can be provided betweentubular 120 and the sidewall of wellbore 114. In some embodiments, theoperating environment 100 includes a workover and/or drilling rigpositioned at the surface and extending over the wellbore 114.

In general, the wellbore 114 can be drilled into the subterraneanformation 102 using any suitable drilling technique. The wellbore 114can extend substantially vertically from the earth's surface over avertical wellbore portion, deviate from vertical relative to the earth'ssurface over a deviated wellbore portion, and/or transition to ahorizontal wellbore portion. In general, all or portions of a wellboremay be vertical, deviated at any suitable angle, horizontal, and/orcurved. In addition, the wellbore 114 can be a new wellbore, an existingwellbore, a straight wellbore, an extended reach wellbore, a sidetrackedwellbore, a multi-lateral wellbore, and other types of wellbores fordrilling and completing one or more production zones. As illustrated,the wellbore 114 includes a substantially vertical producing section150, which is an open hole completion (e.g., casing 112 does not extendthrough producing section 150). Although section 150 is illustrated as avertical and open hole portion of wellbore 114 in FIG. 1, embodimentsdisclosed herein can be employed in sections of wellbores having anyorientation, and in open or cased sections of wellbores. The casing 112extends into the wellbore 114 from the surface 113 and is cementedwithin the wellbore 114 with cement 111.

Tubular 120 can be lowered into wellbore 114 for performing an operationsuch as drilling, completion, workover, treatment, and/or productionprocesses. In the embodiment shown in FIG. 1, the tubular 120 is acompletion assembly string including a distributed acoustic sensor (DAS)sensor coupled thereto. However, in general, embodiments of the tubular120 can function as a different type of structure in a wellboreincluding, without limitation, as a drill string, casing, liner, jointedtubing, and/or coiled tubing. Further, the tubular 120 may operate inany portion of the wellbore 114 (e.g., vertical, deviated, horizontal,and/or curved section of wellbore 114). Embodiments of DAS systemsdescribed herein can be coupled to the exterior of the tubular 120, asdepicted in FIG. 4B, or in some embodiments, disposed within an interiorof the tubular 120, as shown in FIG. 4A. When the DAS fibre is coupledto the exterior of the tubular 120, as depicted in FIG. 4B, the DAS canbe positioned within a control line, control channel, or recess in thetubular 120. In some embodiments, a sand control system can include anouter shroud to contain the tubular 120 and protect the system duringinstallation. A control line or channel can be formed in the shroud andthe DAS system can be placed in the control line or channel. In someembodiments, the tubular and/or the DAS fiber can be removed prior to orsubsequent utilization of the DAS system as described herein to identifya (first) fluid flow location, followed by removal prior to setting abarrier at or above the identified (first) fluid flow location.

The tubular 120 extends from the surface to the producing zones andgenerally provides a conduit for fluids to travel from the formation 102to the surface. A completion assembly including the tubular 120 caninclude a variety of other equipment or downhole tools to facilitate theproduction of the formation fluids from the production zones. Forexample, zonal isolation devices 117 are used to isolate the variouszones within the wellbore 114. In this embodiment, each zonal isolationdevice 117 can be a packer (e.g., production packer, gravel pack packer,frac-pac packer, etc.). The zonal isolation devices 117 can bepositioned between the screen assemblies 118, for example, to isolatedifferent gravel pack zones or intervals along the wellbore 114 fromeach other. In general, the space between each pair of adjacent zonalisolation devices 117 defines a production interval.

The screen assemblies 118 provide sand control capability. Inparticular, the sand control screen elements 118, or other filter mediaassociated with wellbore tubular 120, can be designed to allow fluids toflow therethrough but restrict and/or prevent particulate matter ofsufficient size from flowing therethrough. In some embodiments, gravelpacks 122 can be formed in the annulus 119 between the screen elements118 (or tubular 120) and the sidewall of the wellbore 114 in an openhole completion. In general, the gravel packs 122 comprise relativelycoarse granular material placed in the annulus to form a rough screenagainst the ingress of sand into the wellbore while also supporting thewellbore wall. The gravel pack 122 is optional and may not be present inall completions.

The fluid flowing into the tubular 120 may comprise more than one fluidcomponent. Typical components include natural gas, oil, water, steam,and/or carbon dioxide. The relative proportions of these components canvary over time based on conditions within the formation 102 and thewellbore 114. Likewise, the composition of the fluid flowing into thetubular 120 sections throughout the length of the entire productionstring can vary significantly from section to section at any given time.

As fluid flows into the wellbore 114 and into the completion assemblystring, the flow of the various fluids into the wellbore 114 and/orthrough the wellbore 114 can create acoustic sounds that can be detectedusing the acoustic sensor such as the DAS system. Each type of fluidflow event such as the different fluid flows and fluid flow locationscan produce an acoustic signature with unique frequency domain features.For example, a fluid flow or “leak” representing fluid flow past arestriction, through an annulus, and/or through the formation can createunique sound profiles over a frequency domain such that each event mayhave a unique acoustic signature based on a plurality of frequencydomain features. In some embodiments, the event or acoustic signaturecan comprise thresholds or ranges for a plurality of different frequencydomain features, combinations of frequency domain features, ormodifications of a plurality of frequency domain features.

In FIG. 1, the DAS comprises an optical fibre 162 based acoustic sensingsystem that uses the optical backscatter component of light injectedinto the optical fibre for detecting acoustic/vibration perturbations(e.g., dynamic strain) along the length of the fibre 162. The light canbe generated by a light generator or source 166 such as a laser, whichcan generate light pulses. The optical fibre 162 acts as the sensorelement with no addition transducers in the optical path, andmeasurements can be taken along the length of the entire optical fibre162. The measurements can then be detected by an optical receiver suchas sensor 164 and selectively filtered to obtain measurements from agiven depth point or range, thereby providing for a distributedmeasurement that has selective data for a plurality of zones along theoptical fibre 162 at any given time. In this manner, the optical fibre162 effectively functions as a distributed array of acoustic sensorsspread over the entire length of the optical fibre 162, which typicallyspans at least a portion of the production zone 150 of the wellbore 114,to detect downhole acoustic signals/vibration perturbations. When usedin an abandonment system, the DAS system can span a portion of thewellbore between a lower zonal isolation device (e.g., a plug, etc.) anda zone desired to be isolated as part of the abandonment process.

The light reflected back up the optical fibre 162 as a result of thebackscatter can travel back to the source, where the signal can becollected by a sensor 164 and processed (e.g., using a processor 168).In general, the time the light takes to return to the collection pointis proportional to the distance traveled along the optical fibre 162.The resulting backscattered light arising along the length of theoptical fibre 162 can be used to characterize the environment around theoptical fibre 162. The use of a controlled light source 166 (e.g.,having a controlled spectral width and frequency) may allow thebackscatter to be collected and any disturbances along the length of theoptical fibre 162 to be analyzed. In general, any acoustic or dynamicstrain disturbances along the length of the optical fibre 162 can resultin a change in the properties of the backscattered light, allowing for adistributed measurement of both the acoustic magnitude, frequency and insome cases of the relative phase of the disturbance.

An acquisition device 160 can be coupled to one end of the optical fibre162. As discussed herein, the light source 166 can generate the light(e.g., one or more light pulses), and the sensor 164 can collect andanalyze the backscattered light returning up the optical fibre 162. Insome contexts, the acquisition device 160 including the light source 166and the sensor 164 can be referred to as an interrogator. In addition tothe light source 166 and the sensor 164, the acquisition device 160generally comprises a processor 168 in signal communication with thesensor 164 to perform various analysis steps described in more detailherein. While shown as being within the acquisition device 160, theprocessor can also be located outside of the acquisition device 160including being located remotely from the acquisition device 160. Thesensor 164 can be used to obtain data at various rates and may obtaindata at a sufficient rate to detect the acoustic signals of interestwith sufficient bandwidth. In an embodiment, depth resolution ranges ofbetween about 1 meter and about 10 meters can be achieved.

While the system 100 described herein can be used with a DAS system toacquire an acoustic signal for a location or depth range in the wellbore114, in general, any suitable acoustic signal acquisition system can beused with the processing steps disclosed herein. For example, variousmicrophones or other sensors can be used to provide an acoustic signalat a given location based on the acoustic signal processing describedherein. The benefit of the use of the DAS system is that an acousticsignal can be obtained across a plurality of locations and/or across acontinuous length along the wellbore 114 rather than at discretelocations.

In addition to the DAS system, a surface sensor or sensor system 152 canbe used to obtain additional data for the wellbore. The surface sensorsystem 152 can comprise one or more sensors such as pressure sensors,flow sensors, temperature sensors, and the like. The sensors can detectthe conditions within the tubular 120 and/or in one or more annuli suchas annuli 119. While only a single annulus between the tubular 120 andthe casing 112 is illustrated in FIG. 1, multiple annuli can be present.For example, more than one casing string (also referred to herein as atubular string or casing) can often be set at or near the surface of thewellbore during drilling, which can result in two or more annuli (e.g.,an annulus between the tubular 120 and the casing 112, an annulusbetween a first casing 112 and a second casing, an annulus between acasing string and the wellbore wall, etc.).

In embodiments, the wellbore comprises one or more tubular strings andone or more annuli disposed between: (i) two adjacent tubular strings ofthe one or more tubular strings, (ii) a tubular string of the one ormore tubular strings and the formation 102, or (iii) both (i) and (ii).For example, as depicted in FIG. 2, which is a schematic,cross-sectional illustration of another downhole wellbore environment100A according to embodiments of this disclosure, wellbore environment100A comprises wellbore 114, tubular 120, and first casing 112A, secondcasing 112B, third casing 112C, and fourth casing 112D. As depicted inFIG. 2, a first annulus 119A is positioned between wellbore 114 andfirst casing 112A and second casing 112B. A second annulus 119B ispositioned between second casing 112B and third casing 112C. A thirdannulus 119C is positioned between third casing 112C and fourth casing112D. In embodiments, identifying a fluid flow location comprisesdetermining an annulus of the one or more annuli and a depth at whichthe fluid flow location is present. The fluid flow locations identifiedaccording to this disclosure can comprise, for example, a location offluid flow from the formation 102 into the wellbore 114, a location offlow between the formation 102 and an annulus between a tubular stringor casing and the wellbore wall (e.g., between the formation 102 andfirst annulus 119A, second annulus 119B, or third annulus 119C), or alocation of flow between annuli formed between a plurality of tubularstrings in the wellbore 114 (e.g., between first annulus 119A and secondannulus 119B or between second annulus 119B and third annulus 119C).

As used herein, reference to the term “surface” (113) can refer to alocation above or at the well head (e.g., at the Kelly bushing, rigfloor, etc.), near the ground level, and/or within the first 100 m,within the first 150 m, within the first 200 m, or within about thefirst 300 m along the wellbore as measured from ground level.

Specific spectral signatures can be determined for each fluid flow eventby considering one or more frequency domain features. The resultingspectral signatures can then be used along with processed acousticsignal data to determine if a fluid flow event is occurring at a depthrange of interest. The spectral signatures can be determined byconsidering the different types of movement and flow occurring within awellbore and characterizing the frequency domain features for each typeof movement.

For the flow of gas into the wellbore, the proximity to the opticalfibre 162 can result in a high likelihood that any acoustic signalsgenerated would be detected by the optical fibre 162. The flow of a gasinto the wellbore would likely result in a turbulent flow over a broadfrequency range. For example, the gas flow acoustic signals can bebetween about 0 Hz and about 1000 Hz, or alternatively between about 0Hz and about 500 Hz. An increased power intensity may occur betweenabout 300 Hz and about 500 Hz from increased turbulence in the gas flow.An example of the acoustic signal resulting from the influx of gas intothe wellbore can include frequency filtered acoustic intensity in depthversus time graphs for five frequency bins. The five frequency binsrepresent 5 Hz to 50 Hz, 50 Hz to 100 Hz, 100 Hz to 500 Hz, 500 Hz to2000 Hz, and 2000 Hz to 5000 Hz. The acoustic intensity in the firstthree bins can have frequency ranges up to about 500 Hz, with a nearlyundetectable acoustic intensity in the frequency range above 500 Hz. Atleast a portion of the frequency domain features may not be presentabove 500 Hz, which can help to define the signature of the influx ofgas.

For the flow of a fluid behind a casing in the wellbore, the proximityof the fluid flow to the optical fibre 162 can result in the acousticsignal being detected. The flow behind the casing can generally becharacterized by a flow of fluid through one or more restrictions basedon a generally narrow or small leak path being present. The flow throughsuch a restriction may be characterized by an increase in spectral powerin a frequency range between about 0 Hz to about 300 Hz with a mainenergy contribution in the range of about 0 Hz to about 100 Hz, orbetween about 0 Hz and about 70 Hz.

Based on the expected sound characteristics from the potential acousticsignal sources, the acoustic signature of each fluid flow event can bedefined relative to background noise contributions. Referring again toFIG. 1, the processor 168 within the acquisition device 160 can beconfigured to perform various data processing to detect the presence ofone or more fluid flow events along a length of the wellbore 114. Theacquisition device 160 can comprise a memory 170 configured to store anapplication or program to perform the data analysis. While shown asbeing contained within the acquisition device 160, the memory 170 cancomprise one or more memories, any of which can be external to theacquisition device 160. In an embodiment, the processor 168 can executethe program, which can configure the processor 168 to filter theacoustic data set spatially, determine one or more frequency domainfeatures of the acoustic signal, compare the resulting frequency domainfeature values to the acoustic signatures, and determine whether or nota fluid flow event is occurring at the selected location based on theanalysis and comparison. The analysis can be repeated across variouslocations along the length of the wellbore 114 to determine theoccurrence of one or more fluid flow events and/or fluid flow eventlocations along the length of the wellbore 114.

At the same time, one or more wellbore parameters can be measured withthe sensor system 152. For example, the sensors can be used to detectthe pressure(s), flow rate(s), temperature(s), and the like at one ormore locations at or near the surface of the wellbore and/or within thewellbore. For example, a pressure in the tubular, and one or more annulican be monitored over time. The measurements can be stored with a timestamp and/or stored with the acquired acoustic data set so that the twodata sets can be time correlated after processing the acoustic signal.

When the acoustic sensor comprises a DAS system, the optical fibre 162can return raw optical data in real time or near real time to theacquisition unit 160. In an embodiment, the raw data can be stored inthe memory 170 for various subsequent uses. The sensor 164 can beconfigured to convert the raw optical data into an acoustic data set.Depending on the type of DAS system employed, the optical data may ormay not be phase coherent and may be pre-processed to improve the signalquality (e.g., for opto-electronic noise normalization/de-trendingsingle point-reflection noise removal through the use of medianfiltering techniques or even through the use of spatial moving averagecomputations with averaging windows set to the spatial resolution of theacquisition unit, etc.).

As shown schematically in FIG. 5, an embodiment of a system fordetecting various fluid flow event conditions such as a leak detectioncan comprise a data extraction unit 402, a processing unit 404, aperipheral sensor data correlation unit 408, and/or an output orvisualization unit 406. The system comprises of a DAS interrogator 160connected to the fibre optic cable 162 deployed in the wellbore. Thedata from the DAS interrogator is transmitted in real time to a dataprocessing unit 402 that receives and processes the data in real time.The data processing unit 402 can perform a variety of processing stepson the acoustic sample data. In an embodiment, the acoustic sample canbe noise de-trended. The noise de-trended acoustic variant data can besubjected to an optional spatial filtering step following thepre-processing steps, if present. This is an optional step and helpsfocus primarily on an interval of interest in the wellbore. For example,the spatial filtering step can be used to focus on an interval wherethere is maximum likelihood of fluid flow when a fluid flow event isbeing examined. In an embodiment, the spatial filtering can narrow thefocus of the analysis to a reservoir section and also allow a reductionin data typically of the order of ten times, thereby simplifying thedata analysis operations. The resulting data set produced through theconversion of the raw optical data can be referred to as the acousticsample data.

This type of filtering can provide several advantages in addition to thedata set size reduction. Whether or not the acoustic data set isspatially filtered, the resulting data, for example the acoustic sampledata, used for the next step of the analysis can be indicative of anacoustic sample over a defined depth (e.g., the entire length of theoptical fibre, some portion thereof, or a point source in the wellbore114). In some embodiments, the acoustic data set can comprise aplurality of acoustic samples resulting from the spatial filtering toprovide data over a number of depth ranges. In some embodiments, theacoustic sample may contain acoustic data over a depth range sufficientto capture multiple points of interest. In some embodiments, theacoustic sample data contains information over the entire frequencyrange at the depth represented by the sample. This is to say that thevarious filtering steps, including the spatial filtering, do not removethe frequency information from the acoustic sample data.

The processing unit 402 can also be used to generate and extractacoustic descriptors (e.g., also referred to as frequency domainfeatures herein) from the acoustic data set. In an embodiment, the dataextraction unit 402 can obtain the optical data and perform the initialpre-processing steps to obtain the initial acoustic information from thesignal returned from the sensor in the wellbore. Various analyses can beperformed including frequency domain feature extraction, frequency bandextraction, frequency analysis and/or transformation, intensity and/orenergy calculations, and/or determination of one or more frequencydomain features of the acoustic data. In order to obtain the frequencydomain features, the data processing unit 402 can be further configuredto perform Discrete Fourier transformations (DFT) or a short timeFourier transform (STFT) of the acoustic variant time domain datameasured at each depth section along the fibre or a section thereof tospectrally check the conformance of the acoustic sample data to one ormore acoustic signatures. The spectral conformance check can be used todetermine if the expected signature of an event is present in theacoustic sample data. Spectral feature extraction through time and spacecan be used to determine the spectral conformance and determine if anacoustic signature (e.g., a gas influx signature, fluid flow signature,etc.) is present in the acoustic sample in order to classify the eventswithin the acoustic signal. Within this process, various frequencydomain features can be calculated for the acoustic sample data.

The frequency domain features represent specific properties orcharacteristics of the acoustic signals. While a number of frequencydomain features can be determined for the acoustic sample data, notevery frequency domain feature may be used in the characterization ofeach acoustic signature. In some embodiments, the frequency domainfeatures that are calculated can be a plurality of different frequencydomain features. Some frequency domain features can representtransformed or modified frequency domain features, includingcombinations or mathematical modifications (e.g., ratios,multiplications, formula, etc.) of a plurality of frequency domainfeatures. The term “frequency domain features” is used here to refer tonot only the frequency domain features obtained from the acousticsignal, but also any combinations or modifications thereof.

The use of the frequency domain features to identify one or more fluidflow events has a number of advantages. First, the use of the frequencydomain features results in significant data reduction relative to theraw DAS data stream. Thus, a number of frequency domain features can becalculated to allow for event identification while the remaining datacan be discarded or otherwise stored, while the remaining analysis canperformed using the frequency domain features. Even when the raw DASdata is stored, the remaining processing power is significantly reducedthrough the use of the frequency domain features rather than the rawacoustic data itself. Further, the use of the frequency domain featuresprovides a concise, quantitative measure of the spectral character oracoustic signature of specific sounds pertinent to downhole fluidsurveillance and other applications that may directly be used forreal-time, application-specific signal processing.

As a further consideration in selecting the frequency domain feature(s)for an event, the dimensionality of the frequency domain feature shouldbe compact. A compact representation is desired to decrease thecomputational complexity of subsequent calculations. The frequencydomain feature should also have discriminant power. For example, fordifferent types of audio signals, the selected set of descriptors shouldprovide altogether different values. A measure for the discriminantpower of a feature is the variance of the resulting feature vectors fora set of relevant input signals. Given different classes of similarsignals, a discriminatory descriptor should have low variance insideeach class and high variance over different classes. The frequencydomain feature should also be able to completely cover the range ofvalues of the property it describes. As an example, the chosen set offrequency domain features should be able to completely and uniquelyidentify the signatures of each of the acoustic signals pertaining to aselected downhole surveillance application or event as described herein.Such frequency domain features can include, but are not limited to, thespectral centroid, the spectral spread, the spectral roll-off, thespectral skewness, the root mean square (RMS) band energy (or thenormalized subband energies/band energy ratios), a loudness or total RMSenergy, spectral flatness, spectral scope, spectral kurtosis, a spectralflux, spectral entropy, and a spectral autocorrelation function. Inembodiments, a single frequency domain feature is utilized to determinethe presence (or absence) of a fluid flow event and identify a fluidflow location of the fluid flow event, which information is subsequentlyutilized to locate one or more well barriers during plugging andabandonment operations and/or determine whether or not such pluggingoperations have been successful at reducing or eliminating the fluidflow at the identified fluid flow location. In alternative embodiments,a plurality (e.g., at least two) of different frequency domain featuresare utilized to determine the presence (or absence) of the fluid flowevent and identify the fluid flow location of the fluid flow event,which information is subsequently utilized to locate one or more wellbarriers during plugging and abandonment operations and/or determinewhether or not such plugging operations have been successful at reducingor eliminating the fluid flow at the identified fluid flow location.

The spectral centroid denotes the “brightness” of the sound captured bythe optical fibre 162 and indicates the center of gravity of thefrequency spectrum in the acoustic sample. The spectral centroid can becalculated as the weighted mean of the frequencies present in thesignal, where the magnitudes of the frequencies present can be used astheir weights in some embodiments. The value of the spectral centroid,Ci, of the i^(th) frame of the acoustic signal captured at a spatiallocation on the fibre, may be written as:

$\begin{matrix}{C_{i} = \frac{\sum_{k = 1}^{N}{{f(x)}{X_{i}(k)}}}{\sum_{k = 1}^{N}{X_{i}(k)}}} & \left( {{Eq}.1} \right)\end{matrix}$

Where X_(i)(k), is the magnitude of the short time Fourier transform ofthe i^(th) frame where ‘k’ denotes the frequency coefficient or binindex, N denotes the total number of bins and f(k) denotes the centrefrequency of the bin. The computed spectral centroid may be scaled tovalue between 0 and 1. Higher spectral centroids typically indicate thepresence of higher frequency acoustics and help provide an immediateindication of the presence of high frequency noise. The calculatedspectral centroid can be compared to a spectral centroid threshold orrange for a given event, and when the spectral centroid meets or exceedsthe threshold, the event of interest may be present.

The absolute magnitudes of the computed spectral centroids can be scaledto read a value between zero and one. The turbulent noise generated byother sources such as fluid flow and flow may typically be in the lowerfrequencies (e.g., under about 100 Hz) and the centroid computation canproduce lower values, for example, around or under 0.1 post rescaling.The introduction of fluid or fluid carrying sand can trigger broaderfrequencies of sounds (e.g., a broad band response) that can extend inspectral content to higher frequencies (e.g., up to and beyond 5,000Hz). This can produce centroids of higher values (e.g., between about0.2 and about 0.7, or between about 0.3 and about 0.5), and themagnitude of change would remain fairly independent of the overallconcentration of sanding assuming there is a good signal to noise ratioin the measurement assuming a traditional electronic noise floor (e.g.,white noise with imposed flicker noise at lower frequencies).

The spectral spread can also be determined for the acoustic sample. Thespectral spread is a measure of the shape of the spectrum and helpsmeasure how the spectrum is distributed around the spectral centroid. Inorder to compute the spectral spread, Si, one has to take the deviationof the spectrum from the computed centroid as per the following equation(all other terms defined above):

$\begin{matrix}{S_{i} = \sqrt{\frac{\sum_{k = 1}^{N}{\left( {{f(k)} - C_{i}} \right)^{2}{X_{i}(k)}}}{\sum_{k = 1}^{N}{X_{i}(k)}}}} & \left( {{Eq}.2} \right)\end{matrix}$

Lower values of the spectral spread correspond to signals whose spectraare tightly concentrated around the spectral centroid. Higher valuesrepresent a wider spread of the spectral magnitudes and provide anindication of the presence of a broad band spectral response. Thecalculated spectral spread can be compared to a spectral spreadthreshold or range, and when the spectral spread meets or exceeds thethreshold or falls within the range, the event of interest may bepresent.

The spectral roll-off is a measure of the bandwidth of the audio signal.The Spectral roll-off of the i^(th) frame, is defined as the frequencybin ‘y’ below which the accumulated magnitudes of the short-time Fouriertransform reach a certain percentage value (usually between 85%-95%) ofthe overall sum of magnitudes of the spectrum.

$\begin{matrix}{{\sum_{k = 1}^{y}{❘{X_{i}(k)}❘}} = {\frac{c}{100}{\sum_{k = 1}^{N}{❘{X_{i}(k)}❘}}}} & \left( {{Eq}.3} \right)\end{matrix}$

Where c=85 or 95. The result of the spectral roll-off calculation is abin index and enables distinguishing acoustic events based on dominantenergy contributions in the frequency domain. (e.g., between gas influxand fluid flow, etc.)

The spectral skewness measures the symmetry of the distribution of thespectral magnitude values around their arithmetic mean.

The RMS band energy provides a measure of the signal energy withindefined frequency bins that may then be used for signal amplitudepopulation. The selection of the bandwidths can be based on thecharacteristics of the captured acoustic signal. In some embodiments, asub-band energy ratio representing the ratio of the upper frequency inthe selected band to the lower frequency in the selected band can rangebetween about 1.5:1 to about 3:1. In some embodiments, the sub-bandenergy ratio can range from about 2.5:1 to about 1.8:1, or alternativelybe about 2:1. In some embodiment, selected frequency ranges for a signalwith a 5,000 Hz Nyquist acquisition bandwidth can include: a first binwith a frequency range between 0 Hz and 20 Hz, a second bin with afrequency range between 20 Hz and 40 Hz, a third bin with a frequencyrange between 40 Hz and 80 Hz, a fourth bin with a frequency rangebetween 80 Hz and 160 Hz, a fifth bin with a frequency range between 160Hz and 320 Hz, a sixth bin with a frequency range between 320 Hz and 640Hz, a seventh bin with a frequency range between 640 Hz and 1280 Hz, aneighth bin with a frequency range between 1280 Hz and 2500 Hz, and aninth bin with a frequency range between 2500 Hz and 5000 Hz. Whilecertain frequency ranges for each bin are listed herein, they are usedas examples only, and other values in the same or a different number offrequency range bins can also be used. In some embodiments, the RMS bandenergies may also be expressed as a ratiometric measure by computing theratio of the RMS signal energy within the defined frequency binsrelative to the total RMS energy across the acquisition (Nyquist)bandwidth. This may help to reduce or remove the dependencies on thenoise and any momentary variations in the broadband sound.

The total RMS energy of the acoustic waveform calculated in the timedomain can indicate the loudness of the acoustic signal. In someembodiments, the total RMS energy can also be extracted from thetemporal domain after filing the signal for noise.

The spectral flatness is a measure of the noisiness/tonality of anacoustic spectrum. It can be computed by the ratio of the geometric meanto the arithmetic mean of the energy spectrum value and may be used asan alternative approach to detect broadbanded signals (e.g., such asthose caused by sand ingress). For tonal signals, the spectral flatnesscan be close to 0 and for broader band signals it can be closer to 1.

The spectral slope provides a basic approximation of the spectrum shapeby a linearly regressed line. The spectral slope represents the decreaseof the spectral amplitudes from low to high frequencies (e.g., aspectral tilt). The slope, the y-intersection, and the max and mediaregression error may be used as features.

The spectral kurtosis provides a measure of the flatness of adistribution around the mean value.

The spectral flux is a measure of instantaneous changes in the magnitudeof a spectrum. It provides a measure of the frame-to-frame squareddifference of the spectral magnitude vector summed across allfrequencies or a selected portion of the spectrum. Signals with slowlyvarying (or nearly constant) spectral properties (e.g.: noise) have alow spectral flux, while signals with abrupt spectral changes have ahigh spectral flux. The spectral flux can allow for a direct measure ofthe local spectral rate of change and consequently serves as an eventdetection scheme that could be used to pick up the onset of acousticevents that may then be further analyzed using the feature set above toidentify and uniquely classify the acoustic signal.

The spectral autocorrelation function provides a method in which thesignal is shifted, and for each signal shift (lag) the correlation orthe resemblance of the shifted signal with the original one is computed.This enables computation of the fundamental period by choosing the lag,for which the signal best resembles itself, for example, where theautocorrelation is maximized. This can be useful in exploratorysignature analysis/even for event detection for well integritymonitoring across specific depths where well barrier elements to bemonitored are positioned.

Any of these frequency domain features, or any combination of thesefrequency domain features, can be used to provide an acoustic signaturefor a fluid flow event. In embodiments, a selected set ofcharacteristics can be used to provide the acoustic signature for eachfluid flow event, and/or all of the frequency domain features that arecalculated can be used as a group in characterizing the acousticsignature for a fluid flow event. The specific values for the frequencydomain features that are calculated can vary depending on the specificattributes of the acoustic signal acquisition system, such that theabsolute value of each frequency domain feature can change betweensystems. In some embodiments, the frequency domain features can becalculated for each event based on the system being used to capture theacoustic signal and/or the differences between systems can be taken intoaccount in determining the frequency domain feature values for eachsignature between the systems used to determine the values and thesystems used to capture the acoustic signal being evaluated. Inembodiments, subtraction of a baseline acoustic signal, as described inExample 1, from a flowing acoustic signal can be utilized to decoupleoptical parameter variations, for example, allowing direct comparison(i.e., “like for like” comparison) of difference logs (e.g., allowingcomparison of sample data sets comprising the flowing acoustic signalfrom which the baseline acoustic signal has been subtracted). In thismanner, a scaling can be effected without the need for anautocalibration each time the DAS sensor is removed and redeployedwithin the wellbore.

In order to obtain the frequency domain features, the acoustic sampledata can be converted to the frequency domain. In an embodiment, the rawoptical data may contain or represent acoustic data in the time domain.A frequency domain representation of the data can be obtained using aFourier Transform. Various algorithms can be used as known in the art.In some embodiments, a Short Time Fourier Transform technique or aDiscrete Time Fourier transform can be used. The resulting data samplemay then be represented by a range of frequencies relative to theirpower levels at which they are present. The raw optical data can betransformed into the frequency domain prior to or after the applicationof the spatial filter. In general, the acoustic sample will be in thefrequency domain in order to determine the spectral centroid and thespectral spread. In an embodiment, the processor 168 can be configuredto perform the conversion of the raw acoustic data and/or the acousticsample data from the time domain into the frequency domain. In theprocess of converting the signal to the frequency domain, the poweracross all frequencies within the acoustic sample can be analyzed. Theuse of the processor 168 to perform the transformation may provide thefrequency domain data in real time or near real time.

The data processing unit 402 can then be used to analyze the acousticsample data in the frequency domain to obtain one or more of thefrequency domain features and provide an output with the determinedfrequency domain features for further processing. In some embodiments,the output of the frequency domain features can include features thatare not used to determine the presence of every event.

The output of the processor with the frequency domain features for theacoustic sample data can then be used to determine the presence of oneor more fluid flow events at one or more locations in the wellborecorresponding to depth intervals over which the acoustic data isacquired or filtered. In some embodiments, the determination of thepresence of one or more fluid flow events can include comparing thefrequency domain features with the frequency domain feature thresholdsor ranges in each fluid flow event signature. When the frequency domainfeatures in the acoustic sample data match one or more of the fluid flowevent signatures, the event can be identified as having occurred duringthe sample data measurement period, which can be in real time. Variousoutputs can be generated to display or indicate the presence (orabsence) of the one or more fluid flow events.

The processed acoustic data (i.e., the frequency domain features), whichcan have a significantly smaller file size (typically over 1000×smaller) can then be written into a file (e.g., an ASCII file) in amemory at certain intervals (e.g., every second, every ten seconds,etc.), which can then be retrieved and transmitted through network usinga data collection and transmission software. This process can beexecuted in real time or near real time for transmission of data.

The data transmitted from the DAS interrogator (which can include thefrequency domain feature data) can then be further processed using asequence of data processing steps as shown in the processing sequence404 in FIG. 5. The processing sequence 404 can comprise a series ofsteps including an event detection step, a signature extraction step, anevent classification step, a leak or fluid flow identification step, andan output step. The descriptor data are first processed using anevent-detection algorithm to determine the presence of any anomalousacoustic response(s) that may be triggered by a fluid leak/flow. Whilethere are several ways to implement the event detection algorithm,amplitude thresholding of the data relative to surface noise captured bythe DAS on the fibre optic cable dispersed at or near the surface (e.g.,within the first 100 meters) of the well head can be used. As an exampleof amplitude thresholding, an acoustic intensity over the entirebandwidth can be averaged over the surface or near surface measurements(e.g., in the first 300 m of acoustic data) acquisitions to provide anestimate of the average surface acoustic noise. A threshold can then betaken as a percentage of this average. For example, the amplitudethreshold can be between about 90% and about 95% of the average. Thepresence of the signal within the wellbore can be detected when theamplitude of the acoustic event captured exceeds the threshold value.The frequency and amplitude characteristics of the surface noise mayalso be used to suppress and/or reduce the background noise within theselected window to identify presence of signals at the surface, ifneeded. This enables a zero point depth recognition, helps to reduce oreliminate surface noise contributions, helps to reduce or eliminate theDAS interrogator noise contributions, allows for the capture of acousticevents and renders the captured events in a format ready for signaturerecognition, and uses processed data (as compared to raw DAS data) asthe primary feed to the processing sequence. While amplitudethresholding is used, other time based digital processing approachescould also be used.

Once the data is initially processed, the anomalous events can berecognized (e.g., as events having amplitudes over the thresholds), andthe corresponding data from the portion of the acoustic sample can beextracted as a depth-time event block. FIG. 6A illustrates an example ofa depth-time event block show depth versus amplitude. Once thedepth-time blocks are amplitude thresholded, the corresponding data mayappear as shown in FIG. 6B, with the surface noise filtered out and theanomalous events highlighted.

In the second step 412 of the processing sequence 404, the acousticevent blocks can be further analyzed by extracting the frequency domainfeatures at the event depths and times identified by the anomalous eventdetection step and comparing the extracted frequency domain features tothe fluid flow event signatures to match the frequency domain featuresfor each identified event with an appropriate signature. The extractionof the frequency domain features can be performed prior to the databeing sent to the processing sequence such that the extraction of thefrequency domain features involves filtering the received frequencydomain features for the depth and times identified by the anomalousevent detection, or the extraction of the frequency domain features canbe performed only after the anomalous depth-time blocks have beenidentified.

In either case, the resulting frequency domain features can be comparedwith one or more fluid flow event signatures to identify if integrityfluid flow event has occurred in the event classification step 414. Insome embodiments, the fluid flow event signatures can include frequencydomain signatures for a liquid leak/flow, a gas leak/flow, or anothersuch event (e.g., an unrecognized event category or other non-flowsignature, which can be used for comparison).

The event classification step 414 can be executed at each depth locationalong the fibre and may depend on the acoustic signatures captured atthe locations identified to have an anomalous event. Once classifiedinto the appropriate category, the intensities of the events can bedetermined using the normalized RMS values within the appropriatefrequency bands extracted on site (e.g., which can already be one of thedescriptors obtained in the extracted frequency domain features) fromthe raw acoustic data. The descriptor data can then be transformed andre-written as an event matrix. These steps can be executed in near realtime at the data integration server, and the transformed decision readywell integrity event data can be stored along with some or all of theacoustic descriptor data. The classified event data may also bevisualized as a three dimensional depth versus time versus event typeintensity plot as shown in FIG. 7A and FIG. 7B to illustrate fluid flowevents as a function of depth and time.

The fluid flow event matrix may be further filtered to highlight andvisualize certain types of fluid flow events as shown in FIG. 7C. Thesemay also be aligned in depth to the well completion schematic and/or thegeological maps (e.g., discrete pressure zones) to ascertain the sourceof the leaking fluid in case of liquid leaks. In embodiments, a fluidflow event or location (e.g., depth) is correlated with one or morestructural features within the wellbore, and a source of the fluid flowdetermined based on the correlating of the one or more fluid flow eventsor locations (e.g., depths) with the one or more structural features.

Accordingly, in embodiments of this disclosure, subsequent to detectionof a leak (e.g., a fluid flow) utilizing the feature extraction and theevent signatures as described hereinabove, a flow log can be determinedusing a feature that is representative of the turbulent noise caused bythe leak. Such a feature representative of the turbulent noise cancomprise, for example, acoustic power, spectral intensity, and the likein the frequency bands identified for the fluid flow. For example, inembodiments, the leak detection and identification step 416, the eventmatrix may also be processed further to obtain semi-quantitative leakassessment by filtering the event matrix to extract the eventscorrelating to gas or liquid leaks/flows and then integrating thefiltered intensity data through time to provide fluid flow logs, anexample of which is shown in FIG. 8.

In producing a visualization fluid flow log, the RMS spectral energy fordepth sections that do not exhibit the spectral conformance to specificfluid flow events can be set to zero. This allows those depth points orzones having one or more frequency domain features greater than thethresholds to be easily observed. FIG. 8 represents an example of anembodiment of a fluid flow log showing acoustic intensity against depth.This figure illustrates the locations having fluid flow as peaks in theacoustic intensity. The acoustic intensity and its visualization on thefluid flow log can therefore be used to identify the relativecontribution of the fluid flows at different points along the wellbore.For example, it may be possible to determine which zone is contributingthe greatest proportion of the fluid flows, which zone contributes thesecond greatest portion of the fluid flows, and so on. This may alsoallow for correlation of one or more zonal isolation devices, potentialleak/flow locations, and/or fluid flow through the formation along thelength of the wellbore.

The use of the processing sequence 404 can result in a suitableidentification of the fluid flows within the wellbore to be plugged andabandoned. In an optional processing step in the peripheral sensor datacorrelation unit 408, the resulting processed data can be correlatedwith external sensor data such as that provided by a sensor system at ornear the surface of the wellbore. This processing sequence may be usedwith the DAS system to determine the flow path for the leaks, especiallyin cases where there are multiple casing strings or leak paths at ornear a depth determined to have a fluid flow. The process may also beused to provide a semi-quantitative estimate of the volumes of fluidassociated with the fluid flow when combined with surface measurements(e.g., bleed off rate measurements, surface pressure gauge data, etc.).

The correlation process can generally comprise the use of changingsurface measurement data as a comparison with the identified fluid flowevent process. For example, changing pressure or flow data at thesurface can be used as a correlation with the fluid flow identificationdata. It may be expected that as the fluid flow occurs, a shut inannulus may have a pressure rise and/or an increased flow rate (e.g., ableed off flow rate). When multiple annuli or leak paths are present,the use of the pressure or flow data can help to identify which leakpath(s) are specifically experiencing the fluid flows, while the fluidflow depth would be known from the fluid flow event detection sequence.While described herein as a leak or fluid flow path, a number ofpotential paths are available for fluid flow within the wellbore. Forexample, a leak can occur past a restriction or barrier in one or moreannuli, between a casing and the formation, and/or within the formationor a hydrocarbon zone, and potentially, into a production assembly. Forexample, fluid flow within a hydrocarbon zone in the formation can bemonitored using any of the methods and systems described herein.

In an embodiment, a correlation process may begin by shutting in a well.This may allow a base reading to be taken of both the surface sensordata and the frequency domain features of the wellbore without fluidflow. Once the baseline readings have been obtained, a leak path can betriggered to potentially induce a fluid flow. For example, an annuluscan be opened to bleed off pressure (e.g., induce a pressuredifferential), which can potentially induce fluid flow within thatannulus if there is a leak in fluid communication with the selectedannulus. This may create a pressure differential between the selectedannulus and a neighboring annulus or annuli. The pressure differentialcan be determined to assess the fluid flow potentials. Once one leakpath has been tested, it can be closed and another leak path can betriggered. This sequence can continue until all of the desired leakpaths that are to be tested are triggered. The DAS monitoring system canremain active during the induced flow process to monitor for leaks andascertain the leaking fluid phase or phases. Inducing the differential(e.g., inducing a first pressure differential prior for determination ofa first fluid flow location prior to setting of a well barrier at orabove the first fluid flow location and/or inducing a second pressuredifferential subsequent setting of the first well barrier at or abovethe first fluid flow location) can comprise, for example, opening a flowvalve within an annulus of the one or more annuli; and inducing a fluidflow based on opening of the flow valve.

In embodiments, a sample data set (e.g., a first sample data set that isa sample of the acoustic signal originating in the wellbore) is obtainedwithin the wellbore by obtaining the baseline acoustic signal data setwhile the wellbore is shut in, inducing the pressure differential withinthe wellbore, as described above, obtaining a flowing acoustic sampledata set while inducing the pressure differential, and subtracting thebaseline acoustic signal data set from the flowing acoustic signal dataset to provide the sample data set from which the plurality of frequencydomain features are determined and utilized as described herein toidentify the fluid flow location within the wellbore.

Once the data is obtained from the sensors and the DAS system, which caninclude the fluid flow event data determined from the processingsequence 404 to determine the presence of absence of any fluid flowevents, the data can be correlated through time to determine a fluidflow location and fluid flow path. For example, the filtered fluid flowacoustic intensities obtained from the processing sequence 404 can beintegrated through time at each depth location to obtain fluid flow data(e.g., which can be visualized as fluid flow logs) for the stages of thefluid flow path triggering (e.g., the annular pressure bleed process).This data can then be aligned in time with the pressures, pressuredifferentials, flow data, etc. for each trigger operation to determinethe fluid flow points and flow paths. For example, it may be determinedthat a given fluid flow path only triggers a fluid flow at a given depthrather than over a number of depths. From this data, the fluid flow logscan be determined for each tubular, casing string, or the like.

In some embodiments, all of the surface sensor data can be used in thisprocess. The pressure data, including the induced pressuredifferentials, may be used to determine the fluid flow paths and fluidflow locations. In embodiments, the bleed off rates can be used toprovide a quantitative assessment of the leak rates from each fluid flowpath. This data can then be stored and/or outputted and used in thefuture for further fluid flow identification, comparison, and/orquantification.

Once a first fluid flow event in a first depth interval (e.g., theentire length of the wellbore or a portion thereof) has been confirmedand the location of the first fluid flow event determined, one or morefirst well barriers can be set in an attempt to plug the well. Anybarriers known to those of skill in the art and with the help of thisdisclosure can be utilized. By way of non-limiting examples, the one ormore well barriers can comprise bridge plugs, packers, cement plugs orcolumns, or combinations thereof, and the like. The acoustic sensor canbe removed from the wellbore 114 prior to the setting of the one or morefirst well barriers employed in an attempt to plug the first fluid flowat the first fluid flow location.

Subsequent to the setting of the first well barrier(s), the fluid flowdetection process can be repeated. That is, the acoustic sensor can bere-deployed into the well within a second depth interval overlapping thefirst depth interval (e.g., generally, a depth interval comprising atleast a portion of the first depth interval and above a location(s) atwhich the one or more first well barriers have been positioned). Whenthe acoustic sensor is redeployed, the well barrier can block theability to deploy the fiber below the well barrier. As a result, theacoustic sensor can be deployed to extend between a point at or near thewell barrier towards the surface of the wellbore. Once redeployed, asecond sample data set can be obtained and utilized as describedhereinabove to identify whether or not a fluid flow rate or mechanismwas reduced or eliminated and/or to determine a second fluid flowlocation. If a second fluid flow presence and a second fluid flowlocation are determined, one or more second well barriers can be set inan attempt to plug the fluid flow at the second fluid flow location.Again, the acoustic sensor can be removed from the wellbore prior to thesetting of the one or more second well barriers employed in the attemptto plug the second fluid flow at the second fluid flow location. Theprocess can be repeated as necessary to prepare the well forabandonment. Accordingly, in embodiments of this disclosure, fluid flowlogs can be compared (“like for like”), and the effectiveness of a flowbarrier validated. That is, a barrier that is placed can be validatedbased on the identified reduction or elimination of the fluid flow rateand/or the fluid flow mechanism at the first fluid flow location. Inembodiments, an effective barrier is one that reduces the fluid inflowat the fluid inflow location such that a flow rate of the/any remainingfluid flow at the inflow location being blocked by the barrier is lessthan 80, 85, 90, 95, 96, 97, 98, 99, or 100% of the original flow rateof the leak (e.g., the original fluid flow rate), or that the fluid flowrate is zero or substantially zero after placement of the barrier.

For example, as depicted in FIGS. 3A and 3B, which are schematics of awellbore environment 100B prior to placement of well barriers, and awellbore environment 100C after placement of well barriers,respectively, tubular 120 can be removed from the wellbore 114 and oneor more well barriers set, at a fluid flow location determined with theuse of the DAS system as described herein, to plug the well forabandonment. In some embodiments, a baseline acoustic signal can beobtained and the first well barrier can be set on the basis of theproducing zone within the wellbore such that the well barrier willgenerally extend through and above the producing zone. Thus, the DASsystem can be utilized s described herein to determine one or morelocations of fluid flow at or above which one or more well barriers canbe positioned.

The DAS system can also be utilized to determine whether or not thefluid flow has been reduced or eliminated by the setting of the one ormore well barriers. By way of example, in the embodiment of FIG. 3B, afirst barrier comprising a first cement plug 130A has been set at afirst location in the wellbore, wherein the first location is withinfirst casing 112A, a second barrier comprising a first bridge plug 131Aand a second cement plug 130B is positioned at a second location in thewellbore, wherein the second location is above the first location andwithin second casing 112B, and a third barrier comprising a secondbridge plug 131B and a third cement plug 130 C has been set at a thirdlocation in the wellbore, wherein the third location is above the secondlocation and within third casing 112C. The DAS system can be utilized asdescribed herein to determine a location at or above which to set thefirst well barrier comprising the first cement plug 130A, the secondwell barrier comprising the first bridge plug 131A and the second cementplug 130B, and/or the third well barrier comprising the second bridgeplug 131B and the third cement plug 130C. Alternatively or additionally,the DAS system can be utilized as described herein to determine if thesetting of the first well barrier comprising the first cement plug 130A,the second well barrier comprising the first bridge plug 131A and thesecond cement plug 130B, and/or the third well barrier comprising thesecond bridge plug 131B and the third cement plug 130C has reduced oreliminated fluid flow. In aspects, any number of well barriers can bepositioned within the wellbore environment, with one or more of the wellbarriers positioned at or above a fluid flow location determined via theDAS system as described herein.

In some embodiments, the fluid flow can be a leak path behind a casingor within an annulus (e.g., between casing strings and/or between acasing string and a wellbore wall). The identification of the locationof the fluid flow may be allow for a separate procedure to be identifiedand performed to stop the fluid flow. For example, a fluid flow behind acasing can be addressed through the use of a repair process comprisingperforating the casing and injecting cement behind the casing (e.g., asqueeze cement procedure). This may be in addition to setting a wellbarrier within the wellbore.

Also provided herein is a method of comparing acoustic signals obtainedbetween different acoustic sensor operations or deployments in awellbore. For example, the method can allow for the comparison between afirst acoustic signal from a first deployment of the fiber and a secondacoustic signal from a second deployment of the fiber after a wellbarrier has been placed in the wellbore. While described in the contextof being redeployed after a well barrier has been placed in thewellbore, the method can allow for a comparison between acoustic signalsobtained between any deployments of the fiber, regardless of whether ornot there are changes made within the wellbore or not.

The method comprises obtaining a first baseline sample data set over afirst depth interval within a wellbore, as described herein. The firstbaseline data set can be a sample of an acoustic signal originatingwithin the wellbore. In some embodiments, the baseline data set can beobtained when the wellbore is shut-in and/or when a stable pressure ismaintained within the wellbore. At least one frequency domain feature ofthe first baseline sample data set can be determined. A first pressuredifferential can be induced within the wellbore, as described herein, toprovide for a fluid flow. A first acoustic data set can be obtained overthe first depth interval within the wellbore while inducing the firstpressure differential, as described herein. At least one frequencydomain feature of the first acoustic data set can then be determined.The at least one frequency domain feature of the first baseline sampledata set can be subtracted from the at least one frequency domainfeature of the first acoustic data set to obtain a first sample data setover the first depth interval. A second baseline sample data set can beobtained over a second depth interval within the wellbore, as describedherein. The second baseline sample data set can be a sample of anacoustic signal originating within the wellbore, and the second depthinterval can overlap with the first depth interval. At least onefrequency domain feature of the second baseline sample data set can bedetermined, as described herein. A second pressure differential can beinduced within the wellbore, as described herein, and a second acousticdata set can be obtained over the second depth interval within thewellbore while inducing the second pressure differential, as describedherein. At least one frequency domain feature of the second acousticdata set can be determined, as described herein, and the at least onefrequency domain feature of the second baseline sample data set can besubtracted from the at least one frequency domain feature of the secondacoustic data set to obtain a second sample data set over the seconddepth interval. The second sample data set can be compared to the firstsample data set over the second depth interval. In some embodiments, afluid flow reduction can be determined at a fluid flow location based oncomparing the second sample data set to the first sample data set. Asnoted hereinabove, the first baseline sample data set and the firstacoustic data set can be obtained with an acoustic sensor disposed inthe wellbore within the first depth interval, and the second baselinesample data set and the second acoustic data set can be obtained withthe acoustic sensor disposed in the wellbore within the second depthinterval. The method can thus further comprise removing the acousticsensor from the wellbore between obtaining the first baseline sampledata set and obtaining the second baseline sample data set (e.g., theacoustic sensor 164 can be removed from the wellbore 114 prior tosetting a well barrier element (e.g., a cement plug 130A/130B/130Cand/or a bridge plug 131A/131B) and/or performing a workover procedureto reduce the fluid flow in an attempt to plug fluid flow at anidentified fluid flow location, and redeployed in the wellbore 114subsequent the setting of the well barrier element).

Any of the systems and methods disclosed herein can be carried out on acomputer or other device comprising a processor, such as the acquisitiondevice 160 of FIG. 1. FIG. 9 illustrates a computer system 780 suitablefor implementing one or more embodiments disclosed herein such as theacquisition device or any portion thereof. The computer system 780includes a processor 782 (which may be referred to as a centralprocessor unit or CPU) that is in communication with memory devicesincluding secondary storage 784, read only memory (ROM) 786, randomaccess memory (RAM) 788, input/output (I/O) devices 790, and networkconnectivity devices 792. The processor 782 may be implemented as one ormore CPU chips.

It is understood that by programming and/or loading executableinstructions onto the computer system 780, at least one of the CPU 782,the RAM 788, and the ROM 786 are changed, transforming the computersystem 780 in part into a particular machine or apparatus having thenovel functionality taught by the present disclosure. It is fundamentalto the electrical engineering and software engineering arts thatfunctionality that can be implemented by loading executable softwareinto a computer can be converted to a hardware implementation bywell-known design rules. Decisions between implementing a concept insoftware versus hardware typically hinge on considerations of stabilityof the design and numbers of units to be produced rather than any issuesinvolved in translating from the software domain to the hardware domain.Generally, a design that is still subject to frequent change may bepreferred to be implemented in software, because re-spinning a hardwareimplementation is more expensive than re-spinning a software design.Generally, a design that is stable that will be produced in large volumemay be preferred to be implemented in hardware, for example in anapplication specific integrated circuit (ASIC), because for largeproduction runs the hardware implementation may be less expensive thanthe software implementation. Often a design may be developed and testedin a software form and later transformed, by well-known design rules, toan equivalent hardware implementation in an application specificintegrated circuit that hardwires the instructions of the software. Inthe same manner as a machine controlled by a new ASIC is a particularmachine or apparatus, likewise a computer that has been programmedand/or loaded with executable instructions may be viewed as a particularmachine or apparatus.

Additionally, after the system 780 is turned on or booted, the CPU 782may execute a computer program or application. For example, the CPU 782may execute software or firmware stored in the ROM 786 or stored in theRAM 788. In some cases, on boot and/or when the application isinitiated, the CPU 782 may copy the application or portions of theapplication from the secondary storage 784 to the RAM 788 or to memoryspace within the CPU 782 itself, and the CPU 782 may then executeinstructions that the application is comprised of. In some cases, theCPU 782 may copy the application or portions of the application frommemory accessed via the network connectivity devices 792 or via the I/Odevices 790 to the RAM 788 or to memory space within the CPU 782, andthe CPU 782 may then execute instructions that the application iscomprised of. During execution, an application may load instructionsinto the CPU 782, for example load some of the instructions of theapplication into a cache of the CPU 782. In some contexts, anapplication that is executed may be said to configure the CPU 782 to dosomething, e.g., to configure the CPU 782 to perform the function orfunctions promoted by the subject application. When the CPU 782 isconfigured in this way by the application, the CPU 782 becomes aspecific purpose computer or a specific purpose machine.

The secondary storage 784 is typically comprised of one or more diskdrives or tape drives and is used for non-volatile storage of data andas an over-flow data storage device if RAM 788 is not large enough tohold all working data. Secondary storage 784 may be used to storeprograms which are loaded into RAM 788 when such programs are selectedfor execution. The ROM 786 is used to store instructions and perhapsdata which are read during program execution. ROM 786 is a non-volatilememory device which typically has a small memory capacity relative tothe larger memory capacity of secondary storage 784. The RAM 788 is usedto store volatile data and perhaps to store instructions. Access to bothROM 786 and RAM 788 is typically faster than to secondary storage 784.The secondary storage 784, the RAM 788, and/or the ROM 786 may bereferred to in some contexts as computer readable storage media and/ornon-transitory computer readable media.

I/O devices 790 may include printers, video monitors, liquid crystaldisplays (LCDs), touch screen displays, keyboards, keypads, switches,dials, mice, track balls, voice recognizers, card readers, paper tapereaders, or other well-known input devices.

The network connectivity devices 792 may take the form of modems, modembanks, Ethernet cards, universal serial bus (USB) interface cards,serial interfaces, token ring cards, fibre distributed data interface(FDDI) cards, wireless local area network (WLAN) cards, radiotransceiver cards that promote radio communications using protocols suchas code division multiple access (CDMA), global system for mobilecommunications (GSM), long-term evolution (LTE), worldwideinteroperability for microwave access (WiMAX), near field communications(NFC), radio frequency identity (RFID), and/or other air interfaceprotocol radio transceiver cards, and other well-known network devices.These network connectivity devices 792 may enable the processor 782 tocommunicate with the Internet or one or more intranets. With such anetwork connection, it is contemplated that the processor 782 mightreceive information from the network, or might output information to thenetwork (e.g., to an event database) in the course of performing theabove-described method steps. Such information, which is oftenrepresented as a sequence of instructions to be executed using processor782, may be received from and outputted to the network, for example, inthe form of a computer data signal embodied in a carrier wave.

Such information, which may include data or instructions to be executedusing processor 782 for example, may be received from and outputted tothe network, for example, in the form of a computer data baseband signalor signal embodied in a carrier wave. The baseband signal or signalembedded in the carrier wave, or other types of signals currently usedor hereafter developed, may be generated according to several methodswell-known to one skilled in the art. The baseband signal and/or signalembedded in the carrier wave may be referred to in some contexts as atransitory signal.

The processor 782 executes instructions, codes, computer programs,scripts which it accesses from hard disk, floppy disk, optical disk(these various disk based systems may all be considered secondarystorage 784), flash drive, ROM 786, RAM 788, or the network connectivitydevices 792. While only one processor 782 is shown, multiple processorsmay be present. Thus, while instructions may be discussed as executed bya processor, the instructions may be executed simultaneously, serially,or otherwise executed by one or multiple processors. Instructions,codes, computer programs, scripts, and/or data that may be accessed fromthe secondary storage 784, for example, hard drives, floppy disks,optical disks, and/or other device, the ROM 786, and/or the RAM 788 maybe referred to in some contexts as non-transitory instructions and/ornon-transitory information.

In an embodiment, the computer system 780 may comprise two or morecomputers in communication with each other that collaborate to perform atask. For example, but not by way of limitation, an application may bepartitioned in such a way as to permit concurrent and/or parallelprocessing of the instructions of the application. Alternatively, thedata processed by the application may be partitioned in such a way as topermit concurrent and/or parallel processing of different portions of adata set by the two or more computers. In an embodiment, virtualizationsoftware may be employed by the computer system 780 to provide thefunctionality of a number of servers that is not directly bound to thenumber of computers in the computer system 780. For example,virtualization software may provide twenty virtual servers on fourphysical computers. In an embodiment, the functionality disclosed abovemay be provided by executing the application and/or applications in acloud computing environment. Cloud computing may comprise providingcomputing services via a network connection using dynamically scalablecomputing resources. Cloud computing may be supported, at least in part,by virtualization software. A cloud computing environment may beestablished by an enterprise and/or may be hired on an as-needed basisfrom a third party provider. Some cloud computing environments maycomprise cloud computing resources owned and operated by the enterpriseas well as cloud computing resources hired and/or leased from a thirdparty provider.

In an embodiment, some or all of the functionality disclosed above maybe provided as a computer program product. The computer program productmay comprise one or more computer readable storage medium havingcomputer usable program code embodied therein to implement thefunctionality disclosed above. The computer program product may comprisedata structures, executable instructions, and other computer usableprogram code. The computer program product may be embodied in removablecomputer storage media and/or non-removable computer storage media. Theremovable computer readable storage medium may comprise, withoutlimitation, a paper tape, a magnetic tape, magnetic disk, an opticaldisk, a solid state memory chip, for example analog magnetic tape,compact disk read only memory (CD-ROM) disks, floppy disks, jump drives,digital cards, multimedia cards, and others. The computer programproduct may be suitable for loading, by the computer system 780, atleast portions of the contents of the computer program product to thesecondary storage 784, to the ROM 786, to the RAM 788, and/or to othernon-volatile memory and volatile memory of the computer system 780. Theprocessor 782 may process the executable instructions and/or datastructures in part by directly accessing the computer program product,for example by reading from a CD-ROM disk inserted into a disk driveperipheral of the computer system 780. Alternatively, the processor 782may process the executable instructions and/or data structures byremotely accessing the computer program product, for example bydownloading the executable instructions and/or data structures from aremote server through the network connectivity devices 792. The computerprogram product may comprise instructions that promote the loadingand/or copying of data, data structures, files, and/or executableinstructions to the secondary storage 784, to the ROM 786, to the RAM788, and/or to other non-volatile memory and volatile memory of thecomputer system 780.

In some contexts, the secondary storage 784, the ROM 786, and the RAM788 may be referred to as a non-transitory computer readable medium or acomputer readable storage media. A dynamic RAM embodiment of the RAM788, likewise, may be referred to as a non-transitory computer readablemedium in that while the dynamic RAM receives electrical power and isoperated in accordance with its design, for example during a period oftime during which the computer system 780 is turned on and operational,the dynamic RAM stores information that is written to it. Similarly, theprocessor 782 may comprise an internal RAM, an internal ROM, a cachememory, and/or other internal non-transitory storage blocks, sections,or components that may be referred to in some contexts as non-transitorycomputer readable media or computer readable storage media.

EXAMPLES

The embodiments having been generally described, the following examplesare given as particular embodiments of the disclosure and to demonstratethe practice and advantages thereof. It is understood that the examplesare given by way of illustration and are not intended to limit thespecification or the claims in any manner.

Example 1

In this Example, a DAS system as described herein was utilized to plug awell for abandonment. FIG. 10 is a schematic showing baseline logs forthree runs: Run 1 prior to placement of a first well barrier element(WBE1), referred to in FIG. 10 as “Pre WBE1 placement; Run 2 afterplacement of WBE1, referred to in FIG. 10 as “Post WBE1 placement”; andRun 3 after placement of second and third well barrier elements(WBE2/3), referred to in FIG. 10 as “Post WBE2/3 placement.” DAS logswere also obtained while inducing a first pressure by bleeding the Bannulus for Run 2 after placement of WBE1 and Run 3 after placement ofWBE2/3, and while inducing a second pressure differential by bleedingthe C annulus for Run 2 after placement of WBE1 and for Run 3 afterplacement of WBE2/3.

FIG. 10 is a schematic showing baseline logs for each of the three runs:Run 1 prior to placement of a first well barrier element (WBE1),referred to in FIG. 10 as “Pre WBE1 placement; Run 2 after placement ofWBE1, referred to in FIG. 10 as “Post WBE1 placement”; and Run 3 afterplacement of the second and third well barrier elements (WBE2/3),referred to in FIG. 10 as “Post WBE2/3 placement.” WBE1 was placed at afirst depth of 9000 feet; WBE 2 was placed at a second depth of about5500 feet; and WBE 3 was also placed at the second depth of about 5500feet. As seen in FIG. 10, little to no acoustic noise was captured inthe baseline data in Run 3, after the placement of WBE2/3, and, asexpected, similar behavior was observed in the acoustic response in Run2 and Run 3. The logs indicate effective barrier performance. Thebaseline logs were obtained by obtaining the signal from the DAS sensorin the wellbore and averaging the relative acoustic amplitude over time.Consistent behavior was observed throughout the baseline loggingduration.

As noted above, subsequent the placement of the second and third wellbarrier elements WBE2/3, another baseline log (e.g., baseline for Run 3)was performed by rerunning the DAS sensor in the well to the seconddepth. A second pressure differential was induced by bleeding the Cannulus (referred to in FIG. 11 as “C Bleed”), and a DAS log obtainedduring the C bleed of Run 3. FIG. 11 is a schematic showing the DAS logs(e.g., the acoustic logs) for the baseline and C bleed of Run 3 aftersetting of WBE2/3. As can be seen in FIG. 11, little to no acousticnoise is observed in the zone above the top of the cement (TOC) ofWBE2/3, and similar behavior to the baseline is observed during the Cbleed.

As noted above, a first pressure differential was induced by performinga bleed of the B annulus (referred to as “B Bleed” in the Figures). FIG.12 is a schematic of the DAS logs obtained during the B bleed of Run 2(e.g., after placement of WBE1) and during the B bleed of Run 3 (e.g.,after placement of WBE2/3). As seen in FIG. 12, little to no acousticnoise is captured in the zone above the TOC of WBE2/3.

FIG. 13 is a schematic showing the DAS logs for the baseline, the Bbleed and the C bleed for Run 3 (e.g., after placement of the second andthird well barrier elements WBE2/3). As seen in FIG. 13, the trendremained the same in the B bleed and no significant noise zones wereobserved.

FIG. 14A is a schematic of the DAS logs for Run 1 (e.g., prior toplacement of first well barrier element WBE1), including one houraveraged comparisons for the baseline, the B bleed, and the C bleed.FIG. 14B is a schematic of the DAS logs for the baseline corrected Cbleed (e.g., the C bleed minus the baseline) of Run 1 and a baselinesmoothed log of the C bleed of Run 1, which was obtained by subtractingthe C bleed from the baseline and then smoothing (e.g., running a medianfilter or moving average). FIG. 15A is a schematic of the DAS logs forRun 3 (e.g., after placement of the second and third well barrierelements WBE2/3), including one hour averaged comparisons for thebaseline, the B bleed, and the C bleed. FIG. 15B is a schematic of theDAS logs for the baseline corrected C bleed (e.g., the C bleed minus thebaseline) of Run 3 and a baseline smoothed log of the C bleed of Run 3.

FIG. 16 is a schematic of the DAS logs of the baseline smoothed C bleedsof Run 1 (e.g., prior to placement of WBE1) and Run 3 (e.g., afterplacement of WBE2/3). As seen in FIG. 16, a reduction in the baselinesmoothed flow noise observed in Runs 1 and 3 evidences a drop in overallflow noise at shallower depths during the bleed, indicating successfulbarrier placement and performance.

While various embodiments have been shown and described, modificationsthereof can be made by one skilled in the art without departing from thespirit and teachings of the disclosure. The embodiments described hereinare exemplary only, and are not intended to be limiting. Many variationsand modifications of the subject matter disclosed herein are possibleand are within the scope of the disclosure. Where numerical ranges orlimitations are expressly stated, such express ranges or limitationsshould be understood to include iterative ranges or limitations of likemagnitude falling within the expressly stated ranges or limitations(e.g., from about 1 to about 10 includes, 2, 3, 4, etc.; greater than0.10 includes 0.11, 0.12, 0.13, etc.). For example, whenever a numericalrange with a lower limit, R_(L) and an upper limit, R_(U) is disclosed,any number falling within the range is specifically disclosed. Inparticular, the following numbers within the range are specificallydisclosed: R=R_(L)+k*(R_(U)−R_(L)), wherein k is a variable ranging from1 percent to 100 percent with a 1 percent increment, i.e., k is 1percent, 2 percent, 3 percent, 4 percent, 5 percent, . . . 50 percent,51 percent, 52 percent, . . . , 95 percent, 96 percent, 97 percent, 98percent, 99 percent, or 100 percent. Moreover, any numerical rangedefined by two R numbers as defined in the above is also specificallydisclosed. Use of the term “optionally” with respect to any element of aclaim is intended to mean that the subject element is required, oralternatively, is not required. Both alternatives are intended to bewithin the scope of the claim. Use of broader terms such as comprises,includes, having, etc. should be understood to provide support fornarrower terms such as consisting of, consisting essentially of,comprised substantially of, etc.

Accordingly, the scope of protection is not limited by the descriptionset out above but is only limited by the claims which follow, that scopeincluding all equivalents of the subject matter of the claims. Each andevery claim is incorporated into the specification as an embodiment ofthe present disclosure. Thus, the claims are a further description andare an addition to the embodiments of the present disclosure. Thediscussion of a reference is not an admission that it is prior art tothe present disclosure, especially any reference that may have apublication date after the priority date of this application. Thedisclosures of all patents, patent applications, and publications citedherein are hereby incorporated by reference, to the extent that theyprovide exemplary, procedural, or other details supplementary to thoseset forth herein.

Additional Description

The particular embodiments disclosed above are illustrative only, as thepresent disclosure may be modified and practiced in different butequivalent manners apparent to those skilled in the art having thebenefit of the teachings herein. Furthermore, no limitations areintended to the details of construction or design herein shown, otherthan as described in the claims below. It is therefore evident that theparticular illustrative embodiments disclosed above may be altered ormodified and all such variations are considered within the scope andspirit of the present disclosure. Alternative embodiments that resultfrom combining, integrating, and/or omitting features of theembodiment(s) are also within the scope of the disclosure. Whilecompositions and methods are described in broader terms of “having”,“comprising,” “containing,” or “including” various components or steps,the compositions and methods can also “consist essentially of” or“consist of” the various components and steps. Use of the term“optionally” with respect to any element of a claim means that theelement is required, or alternatively, the element is not required, bothalternatives being within the scope of the claim.

Numbers and ranges disclosed above may vary by some amount. Whenever anumerical range with a lower limit and an upper limit is disclosed, anynumber and any included range falling within the range are specificallydisclosed. In particular, every range of values (of the form, “fromabout a to about b,” or, equivalently, “from approximately a to b,” or,equivalently, “from approximately a-b”) disclosed herein is to beunderstood to set forth every number and range encompassed within thebroader range of values. Also, the terms in the claims have their plain,ordinary meaning unless otherwise explicitly and clearly defined by thepatentee. Moreover, the indefinite articles “a” or “an”, as used in theclaims, are defined herein to mean one or more than one of the elementthat it introduces. If there is any conflict in the usages of a word orterm in this specification and one or more patent or other documents,the definitions that are consistent with this specification should beadopted.

Embodiments disclosed herein include:

A: A method of abandoning a wellbore, the method comprising: obtaining afirst sample data set within a wellbore, wherein the first sample dataset is a sample of an acoustic signal originating within the wellbore;determining a first plurality of frequency domain features of the firstsample data set; identifying a first fluid flow location within thewellbore using the first plurality of frequency domain features: settinga first barrier at or above the first fluid flow location; obtaining asecond sample data set within the wellbore above the first barrier,wherein the second sample data set is a sample of an acoustic signaloriginating within the wellbore above the first barrier; determining asecond plurality of frequency domain features of the second sample dataset; and identifying that a fluid flow rate or fluid flow mechanism atthe first fluid flow location has been reduced or eliminated and/oridentifying a second fluid flow location within the wellbore using thesecond plurality of frequency domain features.

B: A system for abandoning a wellbore, the system comprising: a receiverunit comprising a processor and a memory, wherein the receiver unit isconfigured to receive an acoustic signal from a sensor disposed in awellbore, wherein a processing application is stored in the memory, andwherein the processing application, when executed on the processor,configures the processor to: receive a first baseline acoustic signaldata set from the sensor, wherein the first baseline acoustic signaldata set comprises an indication of the acoustic signal received over afirst depth interval while the wellbore is shut in; receive a firstflowing acoustic signal data set, wherein the first flowing acousticsignal data set comprises an indication of the acoustic signal receivedover the first depth interval while a first pressure differential isinduced within the wellbore; determine a baseline fluid flow log usingthe first baseline acoustic signal data set; determine a flowing fluidflow log using the first flowing acoustic signal data set; subtract thebaseline fluid flow log from the flowing fluid flow log to provide afirst sample data set; determine a first plurality of frequency domainfeatures of the first sample data set; identify a first fluid flowlocation within the wellbore using the first plurality of frequencydomain features; determine a change in a flow rate or flow mechanism atthe first fluid flow location using the first sample data set; andgenerate an output indicative of the first fluid flow location and achange in the flow rate or flow mechanism at the first fluid flowlocation.

C: A method of comparing acoustic signals obtained between differentacoustic sensor operations in a wellbore, the method comprising:obtaining a first baseline sample data set over a first depth intervalwithin a wellbore, wherein the first baseline data set is a sample of anacoustic signal originating within the wellbore; determining at leastone frequency domain feature of the first baseline sample data set;inducing a first pressure differential within the wellbore; obtaining afirst acoustic data set over the first depth interval within thewellbore while inducing the first pressure differential; determining atleast one frequency domain feature of the first acoustic data set;subtracting the at least one frequency domain feature of the firstbaseline sample data set from the at least one frequency domain featureof the first acoustic data set to obtain a first sample data set overthe first depth interval; obtaining a second baseline sample data setover a second depth interval within the wellbore, wherein the secondbaseline sample data set is a sample of an acoustic signal originatingwithin the wellbore, wherein the second depth interval overlaps with thefirst depth interval; determining at least one frequency domain featureof the second baseline sample data set; inducing a second pressuredifferential within the wellbore; obtaining a second acoustic data setover the second depth interval within the wellbore while inducing thesecond pressure differential; determining at least one frequency domainfeature of the second acoustic data set; subtracting the at least onefrequency domain feature of the second baseline sample data set from theat least one frequency domain feature of the second acoustic data set toobtain a second sample data set over the second depth interval; andcomparing the second sample data set to the first sample data set overthe second depth interval.

D: A system for of comparing acoustic signals obtained between differentacoustic sensor operations in a wellbore, the system comprising: areceiver unit comprising a processor and a memory, wherein the receiverunit is configured to receive an acoustic signal from a sensor disposedin a wellbore, wherein a processing application is stored in the memory,and wherein the processing application, when executed on the processor,configures the processor to: receive a first baseline sample data setover a first depth interval within the wellbore, wherein the firstbaseline data set is a sample of an acoustic signal originating withinthe wellbore; determine at least one frequency domain feature of thefirst baseline sample data set; receive a first acoustic data set overthe first depth interval within the wellbore, wherein the first acousticdata sat is an acoustic signal obtained while a first pressuredifferential is induced within the wellbore; determine at least onefrequency domain feature of the first acoustic data set; subtract the atleast one frequency domain feature of the first baseline sample data setfrom the at least one frequency domain feature of the first acousticdata set to obtain a first sample data set over the first depthinterval; receive a second baseline sample data set over a second depthinterval within the wellbore, wherein the second baseline sample dataset is a sample of an acoustic signal originating within the wellbore,wherein the second depth interval overlaps with the first depthinterval; determine at least one frequency domain feature of the secondbaseline sample data set; receive a second acoustic data set over thesecond depth interval within the wellbore, wherein the second acousticdata sat is an acoustic signal obtained while a second pressuredifferential is induced within the wellbore; determine at least onefrequency domain feature of the second acoustic data set; subtract theat least one frequency domain feature of the second baseline sample dataset from the at least one frequency domain feature of the secondacoustic data set to obtain a second sample data set over the seconddepth interval; and compare the second sample data set to the firstsample data set over the second depth interval; and generate an outputindicative of the comparison between the second sample data set and thefirst sample data set.

E: A method of abandoning a wellbore, the method comprising: obtaining afirst sample data set over a first depth interval within a wellbore,wherein the first sample data set comprises a first acoustic data sethaving a first baseline acoustic sample data set subtracted therefrom,wherein the first acoustic data set is obtained over the first depthinterval while a first pressure differential is induced in the wellbore,and wherein the first baseline acoustic sample data set is obtained overthe first depth interval while the wellbore is shut in; identifying afluid flow location within the first depth interval using the firstsample data set; obtaining a second sample data set over a second depthinterval within a wellbore, wherein the second sample data set isobtained after a barrier is set at or above the fluid flow location,wherein the second sample data set comprises a second acoustic data sethaving a second baseline acoustic sample data set subtracted therefrom,wherein the second acoustic data set is obtained over the second depthinterval while a second pressure differential is induced in thewellbore, wherein the second baseline acoustic sample data set isobtained over the second depth interval while the wellbore is shut in,and wherein the second depth interval is overlaps the first depthinterval: comparing the first sample data set to the second sample dataset; and determining whether or not fluid flow at the fluid flowlocation is substantially blocked by the barrier.

F: A system for abandoning a wellbore, the system comprising: a receiverunit comprising a processor and a memory, wherein the receiver unit isconfigured to receive an acoustic signal from a sensor disposed in awellbore, wherein a processing application is stored in the memory, andwherein the processing application, when executed on the processor,configures the processor to: receive a first baseline acoustic sampledata set and a first acoustic data set from the sensor, wherein thefirst acoustic data set is an acoustic signal obtained over a firstdepth interval while a first pressure differential is induced in thewellbore, and wherein the first baseline acoustic sample data set is anacoustic signal obtained over the first depth interval while thewellbore is shut in, determine a first sample data set over a firstdepth interval within the wellbore, wherein the first sample data setcomprises the first acoustic data set having the first baseline acousticsample data set subtracted therefrom; identify a fluid flow locationwithin the first depth interval using the first sample data set; receivea second baseline acoustic sample data set and a second acoustic dataset from the sensor, wherein the second acoustic data set is an acousticsignal obtained over a second depth interval while a second pressuredifferential is induced in the wellbore and after a barrier is set at orabove the fluid flow location, and wherein the second baseline acousticsample data set is an acoustic signal obtained over the second depthinterval while the wellbore is shut in and after the barrier is set ator above the fluid flow location; determine a second sample data setover the second depth interval within the wellbore, wherein the secondsample data set comprises the second acoustic data set having the secondbaseline acoustic sample data set subtracted therefrom; compare thefirst sample data set to the second sample data set; determine whetheror not fluid flow at the fluid flow location is substantially blocked bythe barrier; and generate an output indicative the determination ofwhether or not the fluid flow at the fluid flow location issubstantially blocked by the barrier.

Each of embodiments A, B, C, D, E, and F may have one or more of thefollowing additional elements: Element 1: further comprising: setting asecond barrier at or above the second fluid flow location; andsubstantially blocking fluid flow from the first fluid flow location andthe second fluid flow location using the first barrier and the secondbarrier. Element 2: wherein at least one of the first sample data set orthe second sample data set is representative of the acoustic signalacross a frequency spectrum. Element 3: wherein obtaining the firstsample data set comprises: obtaining a baseline acoustic signal data setwhile the wellbore is shut in; obtaining a baseline fluid flow log usingthe baseline acoustic signal data set; inducing a pressure differentialwithin the wellbore; obtaining a flowing acoustic signal data set whileinducing the pressure differential; obtaining a flowing fluid flow logusing the flowing acoustic signal data set; and subtracting the baselinefluid flow log from the flowing fluid flow log. Element 4: wherein thewellbore comprises one or more tubular strings and one or more annulidisposed between at least one of: i) two adjacent tubular strings of theone or more tubular strings, ii) a tubular string of the one or moretubular strings and a formation, or iii) both i and ii, and whereininducing the pressure differential comprises releasing a fluid from anannulus of the one or more annuli. Element 5: wherein the baselineacoustic signal data set is a time averaged acoustic data set. Element6: wherein the barrier (e.g., the first barrier, the second barrier, orboth the first barrier and the second barrier) comprise a bridge plug, apacker, a cement plug, or a combination thereof. Element 7: wherein thefirst fluid flow location, the second fluid flow location, or both thefirst fluid flow location and the second fluid flow location comprise: alocation of flow from a formation into the wellbore, a location of flowbetween the formation and an annulus between a tubular string and thewellbore wall, or a location of flow between annuli formed between aplurality of tubular strings in the wellbore. Element 8: whereinidentifying the first fluid flow location comprises comparing the firstplurality of frequency domain features with a fluid flow eventsignature, and/or wherein identifying the second fluid flow locationcomprises comparing the second plurality of frequency domain featureswith a fluid flow event signature. Element 9: further comprising:correlating the first fluid flow location with one or more structuralfeatures within the wellbore; and determining a source of the fluid flowat the first fluid flow location based on the correlating of the firstfluid flow location with the one or more structural features. Element10: wherein the wellbore comprises one or more tubular strings and oneor more annuli disposed between at least one of: i) two adjacent tubularstrings of the one or more tubular strings, ii) a tubular string of theone or more tubular strings and a formation, or iii) both i and ii, andwherein identifying the first fluid flow location or the second fluidflow location comprises determining an annulus of the one or more annuliand a depth at which the first fluid flow location or the second fluidflow location is present. Element 11: wherein the processingapplication, when executed on the processor, further configures theprocessor to: receive a second baseline acoustic signal data set fromwithin the wellbore, wherein the second baseline acoustic signal dataset comprises an indication of the acoustic signal received over asecond depth interval of the wellbore while the wellbore is shut in,subsequent the setting of a barrier at or above the identified firstfluid flow location, wherein the second depth interval overlaps thefirst depth interval; receive a second flowing acoustic signal data set,wherein the second flowing acoustic signal data set comprises anindication of the acoustic signal received over the second depthinterval while a second pressure differential is induced within thewellbore, subsequent the setting of the barrier at or above theidentified first fluid flow location; determine a second baseline fluidflow log using the second baseline acoustic signal data set; determine asecond flowing fluid flow log using the second flowing acoustic signaldata set; subtract the second baseline fluid flow log from the secondflowing fluid flow log to provide a second sample data set; determine asecond plurality of frequency domain features of the second sample dataset; determine that a fluid flow rate or a fluid flow mechanism at thefirst fluid flow location within the wellbore has been reduced oreliminated and/or identify a second fluid flow location using the secondplurality of frequency domain features; and generate an outputindicative of the identified reduction or elimination of the fluid flowat the first fluid flow location and/or indicative of the second fluidflow location. Element 12: The system of claim 12 further comprising:validating the barrier based on the identified reduction or eliminationof fluid flow rate or the fluid flow mechanism at the first fluid flowlocation. Element 13: further comprising: the sensor, wherein the sensorcomprises a fibre optic cable disposed within the wellbore; and anoptical generator coupled to the fibre optic cable, wherein the opticalgenerator is configured to generate a light beam and pass the light beaminto the fibre optic cable. Element 14: wherein the wellbore comprisesone or more tubular strings and one or more annuli disposed between atleast one of: i) two adjacent tubular strings of the one or more tubularstrings, ii) a tubular string of the one or more tubular strings and aformation, or iii) both i and ii, and wherein where the first fluid flowlocation, the second fluid flow location, or both comprise: a locationof flow from a formation into the wellbore, a location of flow betweenthe formation and an annulus between a tubular string and the wellborewall, or a location of flow between annuli formed between a plurality oftubular strings in the wellbore. Element 15: wherein inducing the firstpressure differential and/or inducing the second pressure differentialcomprises: opening a flow valve within an annulus of the one or moreannuli; and inducing a fluid flow based on opening of the flow valve.Element 16: wherein the first pressure differential and/or the secondpressure differential is indicative of a difference in pressure betweenan annulus of the one or more annuli and an adjacent flow path in thewellbore. Element 17: wherein the processing application, when executedon the processor, further configures the processor to: integrate or timeaverage an acoustic intensity within specified frequency bands for fluidflow in the wellbore, and determine a relative fluid flowrate for fluidflow based on the integrated acoustic intensity. Element 18: wherein theoutput comprises a fluid flow log. Element 19: further comprising:determining a fluid flow reduction at a fluid flow location based oncomparing the second sample data set to the first sample data set.Element 20: wherein the first baseline sample data set and the firstacoustic data set are obtained with an acoustic sensor disposed in thewellbore within the first depth interval, wherein the second baselinesample data set and the second acoustic data set are obtained with theacoustic sensor disposed in the wellbore within the second depthinterval, and wherein the method further comprises: removing theacoustic sensor from the wellbore between obtaining the first baselinesample data set and obtaining the second baseline sample data set.Element 21: wherein identifying the fluid flow location within the firstdepth interval using the first sample data set comprises determining aplurality of frequency domain features of the first sample data set.Element 22: wherein the plurality of frequency domain features of thefirst sample data set comprise at least two frequency domain featuresselected from the group consisting of a spectral centroid, a spectralspread, a spectral roll-off, a spectral skewness, an RMS band energy, atotal RMS energy, a spectral flatness, a spectral slope, a spectralkurtosis, a spectral flux, spectral entropy, a spectral autocorrelationfunction, and combinations thereof.

While various embodiments in accordance with the principles disclosedherein have been shown and described above, modifications thereof may bemade by one skilled in the art without departing from the spirit and theteachings of the disclosure. The embodiments described herein arerepresentative only and are not intended to be limiting. Manyvariations, combinations, and modifications are possible and are withinthe scope of the disclosure. Alternative embodiments that result fromcombining, integrating, and/or omitting features of the embodiment(s)are also within the scope of the disclosure. Accordingly, the scope ofprotection is not limited by the description set out above, but isdefined by the claims which follow, that scope including all equivalentsof the subject matter of the claims. Each and every claim isincorporated as further disclosure into the specification and the claimsare embodiment(s) of the present invention(s). Furthermore, anyadvantages and features described above may relate to specificembodiments, but shall not limit the application of such issued claimsto processes and structures accomplishing any or all of the aboveadvantages or having any or all of the above features.

Additionally, the section headings used herein are provided forconsistency with the suggestions under 37 C.F.R. 1.77 or to otherwiseprovide organizational cues. These headings shall not limit orcharacterize the invention(s) set out in any claims that may issue fromthis disclosure. Specifically and by way of example, although theheadings might refer to a “Field,” the claims should not be limited bythe language chosen under this heading to describe the so-called field.Further, a description of a technology in the “Background” is not to beconstrued as an admission that certain technology is prior art to anyinvention(s) in this disclosure. Neither is the “Summary” to beconsidered as a limiting characterization of the invention(s) set forthin issued claims. Furthermore, any reference in this disclosure to“invention” in the singular should not be used to argue that there isonly a single point of novelty in this disclosure. Multiple inventionsmay be set forth according to the limitations of the multiple claimsissuing from this disclosure, and such claims accordingly define theinvention(s), and their equivalents, that are protected thereby. In allinstances, the scope of the claims shall be considered on their ownmerits in light of this disclosure, but should not be constrained by theheadings set forth herein.

Use of broader terms such as comprises, includes, and having should beunderstood to provide support for narrower terms such as consisting of,consisting essentially of, and comprised substantially of. Use of theterm “optionally,” “may,” “might,” “possibly,” and the like with respectto any element of an embodiment means that the element is not required,or alternatively, the element is required, both alternatives beingwithin the scope of the embodiment(s). Also, references to examples aremerely provided for illustrative purposes, and are not intended to beexclusive.

While preferred embodiments have been shown and described, modificationsthereof can be made by one skilled in the art without departing from thescope or teachings herein. The embodiments described herein areexemplary only and are not limiting. Many variations and modificationsof the systems, apparatus, and processes described herein are possibleand are within the scope of the disclosure. For example, the relativedimensions of various parts, the materials from which the various partsare made, and other parameters can be varied. Accordingly, the scope ofprotection is not limited to the embodiments described herein, but isonly limited by the claims that follow, the scope of which shall includeall equivalents of the subject matter of the claims. Unless expresslystated otherwise, the steps in a method claim may be performed in anyorder. The recitation of identifiers such as (a), (b), (c) or (1), (2),(3) before steps in a method claim are not intended to and do notspecify a particular order to the steps, but rather are used to simplifysubsequent reference to such steps.

Also, techniques, systems, subsystems, and methods described andillustrated in the various embodiments as discrete or separate may becombined or integrated with other systems, modules, techniques, ormethods without departing from the scope of the present disclosure.Other items shown or discussed as directly coupled or communicating witheach other may be indirectly coupled or communicating through someinterface, device, or intermediate component, whether electrically,mechanically, or otherwise. Other examples of changes, substitutions,and alterations are ascertainable by one skilled in the art and could bemade without departing from the spirit and scope disclosed herein.

1. A method of abandoning a wellbore, the method comprising: obtaining afirst sample data set within a wellbore, wherein the first sample dataset is a sample of an acoustic signal originating within the wellbore;determining a first plurality of frequency domain features of the firstsample data set; identifying a first fluid flow location within thewellbore using the first plurality of frequency domain features; settinga first barrier at or above the first fluid flow location; obtaining asecond sample data set within the wellbore above the first barrier,wherein the second sample data set is a sample of an acoustic signaloriginating within the wellbore above the first barrier; determining asecond plurality of frequency domain features of the second sample dataset; and identifying that a fluid flow rate or fluid flow mechanism atthe first fluid flow location has been reduced or eliminated and/oridentifying a second fluid flow location within the wellbore using thesecond plurality of frequency domain features.
 2. The method of claim 1,further comprising: setting a second barrier at or above the secondfluid flow location; and substantially blocking fluid flow from thefirst fluid flow location and the second fluid flow location using thefirst barrier and the second barrier.
 3. The method of claim 1, whereinat least one of the first sample data set or the second sample data setis representative of the acoustic signal across a frequency spectrum. 4.The method of claim 1, wherein obtaining the first sample data setcomprises: obtaining a baseline acoustic signal data set while thewellbore is shut in; obtaining a baseline fluid flow log using thebaseline acoustic signal data set; inducing a pressure differentialwithin the wellbore; obtaining a flowing acoustic signal data set whileinducing the pressure differential; obtaining a flowing fluid flow logusing the flowing acoustic signal data set; and subtracting the baselinefluid flow log from the flowing fluid flow log.
 5. The method of claim4, wherein the wellbore comprises one or more tubular strings and one ormore annuli disposed between at least one of: i) two adjacent tubularstrings of the one or more tubular strings, ii) a tubular string of theone or more tubular strings and a formation, or iii) both i and ii, andwherein inducing the pressure differential comprises releasing a fluidfrom an annulus of the one or more annuli.
 6. The method of claim 4,wherein the baseline acoustic signal data set is a time averagedacoustic data set.
 7. The method of claim 1, wherein the first barriercomprises a bridge plug, a packer, a cement plug, or a combinationthereof.
 8. The method of claim 1, wherein the first fluid flowlocation, the second fluid flow location, or both the first fluid flowlocation and the second fluid flow location comprise: a location of flowfrom a formation into the wellbore, a location of flow between theformation and an annulus between a tubular string and the wellbore wall,or a location of flow between annuli formed between a plurality oftubular strings in the wellbore.
 9. The method of claim 1, whereinidentifying the first fluid flow location comprises comparing the firstplurality of frequency domain features with a fluid flow eventsignature, and/or wherein identifying the second fluid flow locationcomprises comparing the second plurality of frequency domain featureswith a fluid flow event signature.
 10. The method of claim 1, furthercomprising: correlating the first fluid flow location with one or morestructural features within the wellbore; and determining a source of thefluid flow at the first fluid flow location based on the correlating ofthe first fluid flow location with the one or more structural features.11. The method of claim 1, wherein the wellbore comprises one or moretubular strings and one or more annuli disposed between at least one of:i) two adjacent tubular strings of the one or more tubular strings, ii)a tubular string of the one or more tubular strings and a formation, oriii) both i and ii, and wherein identifying the first fluid flowlocation or the second fluid flow location comprises determining anannulus of the one or more annuli and a depth at which the first fluidflow location or the second fluid flow location is present.
 12. A systemfor abandoning a wellbore, the system comprising: a receiver unitcomprising a processor and a memory, wherein the receiver unit isconfigured to receive an acoustic signal from a sensor disposed in awellbore, wherein a processing application is stored in the memory, andwherein the processing application, when executed on the processor,configures the processor to: receive a first baseline acoustic signaldata set from the sensor, wherein the first baseline acoustic signaldata set comprises an indication of the acoustic signal received over afirst depth interval while the wellbore is shut in; receive a firstflowing acoustic signal data set, wherein the first flowing acousticsignal data set comprises an indication of the acoustic signal receivedover the first depth interval while a first pressure differential isinduced within the wellbore; determine a baseline fluid flow log usingthe first baseline acoustic signal data set; determine a flowing fluidflow log using the first flowing acoustic signal data set; subtract thebaseline fluid flow log from the flowing fluid flow log to provide afirst sample data set; determine a first plurality of frequency domainfeatures of the first sample data set; identify a first fluid flowlocation within the wellbore using the first plurality of frequencydomain features; determine a change in a flow rate or flow mechanism atthe first fluid flow location using the first sample data set; andgenerate an output indicative of the first fluid flow location and achange in the flow rate or flow mechanism at the first fluid flowlocation.
 13. The system of claim 12, wherein the processingapplication, when executed on the processor, further configures theprocessor to: receive a second baseline acoustic signal data set fromwithin the wellbore, wherein the second baseline acoustic signal dataset comprises an indication of the acoustic signal received over asecond depth interval of the wellbore while the wellbore is shut in,subsequent the setting of a barrier at or above the identified firstfluid flow location, wherein the second depth interval overlaps thefirst depth interval; receive a second flowing acoustic signal data set,wherein the second flowing acoustic signal data set comprises anindication of the acoustic signal received over the second depthinterval while a second pressure differential is induced within thewellbore, subsequent the setting of the barrier at or above theidentified first fluid flow location; determining a second baselinefluid flow log using the second baseline acoustic signal data set;determining a second flowing fluid flow log using the second flowingacoustic signal data set; subtract the second baseline fluid flow logfrom the second flowing fluid flow log to provide a second sample dataset; determine a second plurality of frequency domain features of thesecond sample data set; determine that a fluid flow rate or a fluid flowmechanism at the first fluid flow location within the wellbore has beenreduced or eliminated and/or identify a second fluid flow location usingthe second plurality of frequency domain features; and generate anoutput indicative of the identified reduction or elimination of thefluid flow at the first fluid flow location and/or indicative of thesecond fluid flow location.
 14. The system of claim 12, furthercomprising: validating the barrier based on the identified reduction orelimination of fluid flow rate or the fluid flow mechanism at the firstfluid flow location.
 15. The system of claim 12, further comprising: thesensor, wherein the sensor comprises a fibre optic cable disposed withinthe wellbore; and an optical generator coupled to the fibre optic cable,wherein the optical generator is configured to generate a light beam andpass the light beam into the fibre optic cable.
 16. The system of claim12, wherein the wellbore comprises one or more tubular strings and oneor more annuli disposed between at least one of: i) two adjacent tubularstrings of the one or more tubular strings, ii) a tubular string of theone or more tubular strings and a formation, or iii) both i and ii, andwherein where the first fluid flow location, the second fluid flowlocation, or both comprise: a location of flow from a formation into thewellbore, a location of flow between the formation and an annulusbetween a tubular string and the wellbore wall, or a location of flowbetween annuli formed between a plurality of tubular strings in thewellbore.
 17. The method of claim 16, wherein inducing the firstpressure differential and/or inducing the second pressure differentialcomprises: opening a flow valve within an annulus of the one or moreannuli; and inducing a fluid flow based on opening of the flow valve.18. The system of claim 16, wherein the first pressure differentialand/or the second pressure differential is indicative of a difference inpressure between an annulus of the one or more annuli and an adjacentflow path in the wellbore.
 19. The system of claim 12, wherein theprocessing application, when executed on the processor, furtherconfigures the processor to: integrate or time average an acousticintensity within specified frequency bands for fluid flow in thewellbore, and determine a relative fluid flowrate for fluid flow basedon the integrated acoustic intensity.
 20. The system of claim 12,wherein the output comprises a fluid flow log.
 21. A method of comparingacoustic signals obtained between different acoustic sensor operationsin a wellbore, the method comprising: obtaining a first baseline sampledata set over a first depth interval within a wellbore, wherein thefirst baseline data set is a sample of an acoustic signal originatingwithin the wellbore; determining at least one frequency domain featureof the first baseline sample data set; inducing a first pressuredifferential within the wellbore; obtaining a first acoustic data setover the first depth interval within the wellbore while inducing thefirst pressure differential; determining at least one frequency domainfeature of the first acoustic data set; subtracting the at least onefrequency domain feature of the first baseline sample data set from theat least one frequency domain feature of the first acoustic data set toobtain a first sample data set over the first depth interval; obtaininga second baseline sample data set over a second depth interval withinthe wellbore, wherein the second baseline sample data set is a sample ofan acoustic signal originating within the wellbore, wherein the seconddepth interval overlaps with the first depth interval; determining atleast one frequency domain feature of the second baseline sample dataset; inducing a second pressure differential within the wellbore;obtaining a second acoustic data set over the second depth intervalwithin the wellbore while inducing the second pressure differential;determining at least one frequency domain feature of the second acousticdata set; subtracting the at least one frequency domain feature of thesecond baseline sample data set from the at least one frequency domainfeature of the second acoustic data set to obtain a second sample dataset over the second depth interval; and comparing the second sample dataset to the first sample data set over the second depth interval.
 22. Themethod of claim 21, further comprising: determining a fluid flowreduction at a fluid flow location based on comparing the second sampledata set to the first sample data set.
 23. The method of claim 21,wherein the first baseline sample data set and the first acoustic dataset are obtained with an acoustic sensor disposed in the wellbore withinthe first depth interval, wherein the second baseline sample data setand the second acoustic data set are obtained with the acoustic sensordisposed in the wellbore within the second depth interval, and whereinthe method further comprises: removing the acoustic sensor from thewellbore between obtaining the first baseline sample data set andobtaining the second baseline sample data set.
 24. A system for ofcomparing acoustic signals obtained between different acoustic sensoroperations in a wellbore, the system comprising: a receiver unitcomprising a processor and a memory, wherein the receiver unit isconfigured to receive an acoustic signal from a sensor disposed in awellbore, wherein a processing application is stored in the memory, andwherein the processing application, when executed on the processor,configures the processor to: receive a first baseline sample data setover a first depth interval within the wellbore, wherein the firstbaseline data set is a sample of an acoustic signal originating withinthe wellbore; determine at least one frequency domain feature of thefirst baseline sample data set; receive a first acoustic data set overthe first depth interval within the wellbore, wherein the first acousticdata sat is an acoustic signal obtained while a first pressuredifferential is induced within the wellbore; determine at least onefrequency domain feature of the first acoustic data set; subtract the atleast one frequency domain feature of the first baseline sample data setfrom the at least one frequency domain feature of the first acousticdata set to obtain a first sample data set over the first depthinterval; receive a second baseline sample data set over a second depthinterval within the wellbore, wherein the second baseline sample dataset is a sample of an acoustic signal originating within the wellbore,wherein the second depth interval overlaps with the first depthinterval; determine at least one frequency domain feature of the secondbaseline sample data set; receive a second acoustic data set over thesecond depth interval within the wellbore, wherein the second acousticdata sat is an acoustic signal obtained while a second pressuredifferential is induced within the wellbore; determine at least onefrequency domain feature of the second acoustic data set; subtract theat least one frequency domain feature of the second baseline sample dataset from the at least one frequency domain feature of the secondacoustic data set to obtain a second sample data set over the seconddepth interval; compare the second sample data set to the first sampledata set over the second depth interval; and generate an outputindicative of the comparison between the second sample data set and thefirst sample data set.
 25. A method of abandoning a wellbore, the methodcomprising: obtaining a first sample data set over a first depthinterval within a wellbore, wherein the first sample data set comprisesa first acoustic data set having a first baseline acoustic sample dataset subtracted therefrom, wherein the first acoustic data set isobtained over the first depth interval while a first pressuredifferential is induced in the wellbore, and wherein the first baselineacoustic sample data set is obtained over the first depth interval whilethe wellbore is shut in; identifying a fluid flow location within thefirst depth interval using the first sample data set; obtaining a secondsample data set over a second depth interval within a wellbore, whereinthe second sample data set is obtained after a barrier is set at orabove the fluid flow location, wherein the second sample data setcomprises a second acoustic data set having a second baseline acousticsample data set subtracted therefrom, wherein the second acoustic dataset is obtained over the second depth interval while a second pressuredifferential is induced in the wellbore, wherein the second baselineacoustic sample data set is obtained over the second depth intervalwhile the wellbore is shut in, and wherein the second depth intervaloverlaps the first depth interval; comparing the first sample data setto the second sample data set; and determining whether or not fluid flowat the fluid flow location is substantially blocked by the barrier. 26.The method of claim 25, wherein identifying the fluid flow locationwithin the first depth interval using the first sample data setcomprises determining a plurality of frequency domain features of thefirst sample data set.
 27. The method of claim 26, wherein the pluralityof frequency domain features of the first sample data set comprise atleast two frequency domain features selected from the group consistingof a spectral centroid, a spectral spread, a spectral roll-off, aspectral skewness, an RMS band energy, a total RMS energy, a spectralflatness, a spectral slope, a spectral kurtosis, a spectral flux,spectral entropy, a spectral autocorrelation function, and combinationsthereof.
 28. A system for abandoning a wellbore, the system comprising:a receiver unit comprising a processor and a memory, wherein thereceiver unit is configured to receive an acoustic signal from a sensordisposed in a wellbore, wherein a processing application is stored inthe memory, and wherein the processing application, when executed on theprocessor, configures the processor to: receive a first baselineacoustic sample data set and a first acoustic data set from the sensor,wherein the first acoustic data set is an acoustic signal obtained overa first depth interval while a first pressure differential is induced inthe wellbore, and wherein the first baseline acoustic sample data set isan acoustic signal obtained over the first depth interval while thewellbore is shut in, determine a first sample data set over a firstdepth interval within the wellbore, wherein the first sample data setcomprises the first acoustic data set having the first baseline acousticsample data set subtracted therefrom; identify a fluid flow locationwithin the first depth interval using the first sample data set; receivea second baseline acoustic sample data set and a second acoustic dataset from the sensor, wherein the second acoustic data set is an acousticsignal obtained over a second depth interval while a second pressuredifferential is induced in the wellbore and after a barrier is set at orabove the fluid flow location, and wherein the second baseline acousticsample data set is an acoustic signal obtained over the second depthinterval while the wellbore is shut in and after the barrier is set ator above the fluid flow location; determine a second sample data setover the second depth interval within the wellbore, wherein the secondsample data set comprises the second acoustic data set having the secondbaseline acoustic sample data set subtracted therefrom; compare thefirst sample data set to the second sample data set; and determinewhether or not fluid flow at the fluid flow location is substantiallyblocked by the barrier; and generate an output indicative thedetermination of whether or not the fluid flow at the fluid flowlocation is substantially blocked by the barrier.